2024 推荐系统论文汇总

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文章数量:1658581

对2024年的 推荐系统论文进行一波收集，给各位初学者和算法大佬作为灵感来源，后续专栏会继续更新论文解读，根据评论不断补充，欢迎大家三连~

KDD 2024

转载自：https://zhuanlan.zhihu/p/717644004

跨域推荐 | Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement

研究跨域推荐系统中知识迁移能力的提升，以减少负面迁移的影响。

Zijian Song (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); WenHan Zhang (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); Lifang Deng (Lazada Group); Jiandong Zhang (Lazada Group); Wu Zhihua (Lazada Group); Kaigui Bian (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); Bin Cui (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University)

粗排 | Enhancing Pre-Ranking Performance: Tackling Intermediary Challenges in Multi-Stage Cascading Recommendation Systems

多阶段级联推荐系统中粗排性能的增强，解决中间阶段的挑战。

Jianping Wei (Ant Group); Yujie Zhou (Ant Group); Zhengwei Wu (Ant Group); Ziqi Liu (Ant Group)

生成式出价 | Generative Auto-bidding via Conditional Diffusion Modeling

利用条件扩散模型进行自动出价，提高在线广告的效率。

Jiayan Guo (Peking University, Alibaba Group); Yusen Huo (Alibaba Group); Zhilin Zhang (Alibaba Group); Tianyu Wang (Alibaba Group); Chuan Yu (Alibaba Group); Jian Xu (Alibaba Group); Bo Zheng (Alibaba Group); Yan Zhang (Peking University)

电商相关性模型架构 | Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce

为中文电商设计高效且可解释的相关性架构模型。

Zhe Lin (Alibaba Group); Jiwei Tan (Alibaba Group); Ou Dan (Alibaba Group); Chen Xi (Alibaba Group); Shaowei Yao (Alibaba Group); Bo Zheng (Alibaba Group)

LLM需求理解 | Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs

通过类比推理增强的大型语言模型来更好地理解市场营销人员的需求。

Junjie Wang (Zhejiang University, Ant Group); Dan Yang (Ant Group); Binbin Hu (Ant Group); Yue Shen (Ant Group); Wen Zhang (Zhejiang University); Jinjie Gu (Ant Group)

GCN图协同过滤 | Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks

利用部分顺序图卷积网络进行多行为协同过滤，改善推荐系统。

Yijie Zhang (Jinan University); Yuanchen Bei (Zhejiang University); Hao Chen (The Hong Kong Polytechnic University); Qijie Shen (Alibaba Group); Zheng Yuan (The Hong Kong Polytechnic University); Huan Gong (National University of Defense Technology); Senzhang Wang (Central South University); Feiran Huang (Jinan University); Xiao Huang (The Hong Kong Polytechnic University)

基于蒸馏的跨域推荐 | DDCDR: A Disentangle-based Distillation Framework for Cross-Domain Recommendation

基于解耦和蒸馏的框架用于跨域推荐。

Zhicheng An (Ant Group); Zhexu Gu (Ant Group); Li Yu (Ant Group); Ke Tu (Ant Group); Zhengwei Wu (Ant Group); Binbin Hu (Ant Group); Zhiqiang Zhang (Ant Group); Lihong Gu (Ant Group); Jinjie Gu (Ant Group)

淘宝搜索文本匹配索引 | Text Matching Indexers in Taobao Search

研究淘宝搜索中的文本匹配索引。

Sen Li (Alibaba Group); Fuyu Lv (Alibaba Group); Ruqing Zhang (CAS Key Lab of Network Data Science and Technology, ICT, CAS); Ou Dan (Alibaba Group); Zhixuan Zhang (Alibaba Group); Maarten Rijke (University of Amsterdam)

因果推断 | CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect

针对具有显著处理效应的子群体的因果规则学习。

Jiehui Zhou (State Key Lab of CAD&CG, Zhejiang University, DAMO Academy, Alibaba Group); Linxiao Yang (DAMO Academy, Alibaba Group); Xingyu Liu (State Key Lab of CAD&CG, Zhejiang University); Xinyue Gu (DAMO Academy, Alibaba Group); Liang Sun (DAMO Academy, Alibaba Group); Wei Chen (State Key Lab of CAD&CG, Zhejiang University)

合约广告 | Bi-Objective Contract Allocation for Guaranteed Delivery Advertising

合约广告的双目标合同分配问题。

an Li (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Yundu Huang (Alibaba Group); Wuyang Mao (Alibaba Group); Furong Ye (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences); Xiang He (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Zhonglin Zu (Alibaba Group); Shaowei Cai (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences)

拍卖广告 | Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions

为重复两阶段广告拍卖设计真实性多臂老虎机机制。

Haoming Li (Shanghai Jiaotong University); Yumou Liu (The Chinese University of Hong Kong, Shenzhen); Zhenzhe Zheng (Shanghai Jiao Tong University); Zhilin Zhang (Alibaba Group); Jian Xu (Alibaba Group); Fan Wu (Shanghai Jiao Tong University)

标签推荐 | When Box Meets Graph Neural Network in Tag-aware Recommendation

在标签感知推荐系统中结合盒子模型和图神经网络。

Fake Lin (University of Science and Technology of China); Ziwei Zhao (University of Science and Technology of China); Xi Zhu (University of Science and Technology of China); Da Zhang (University of Science and Technology of China); Shitian Shen (Alibaba Group); Xueying Li (Alibaba Group); Tong Xu (University of Science and Technology of China); Suojuan Zhang (Army Engineering University of PLA); Enhong Chen (University of Science and Technology of China)

Baidu
冷启动CTR预估 | Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions

Yaqing Wang (Baidu Research, Baidu Inc.); Hongming Piao (Department of Computer Science, City University of Hong Kong); Daxiang Dong (Baidu AI Cloud, Baidu Inc.); Quanming Yao (Department of Electronic Engineering, Tsinghua University); Jingbo Zhou (Baidu Research, Baidu Inc.)

图与大模型 | HiGPT: Heterogeneous Graph Language Model

Jiabin Tang (University of Hong Kong); Yuhao Yang (University of Hong Kong); Wei Wei (University of Hong Kong); Lei Shi (Baidu); Long Xia (Baidu Inc.); Dawei Yin (Baidu); Chao Huang (University of Hong Kong)

Bytedance
出价 | Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online Advertising

Yumin Su (ByteDance Inc.); Min Xiang (ByteDance Inc.); Yifei Chen (ByteDance Inc.); Yanbiao Li (ByteDance Inc.); Tian Qin (ByteDance Inc.); Hongyi Zhang (ByteDance Inc.); Yasong Li (ByteDance Inc.); Xiaobing Liu (ByteDance Inc.)

兴趣建模 | Trinity: Syncretizing Multi-/Long-Tail/Long-Term Interests All in One

Jing Yan (ByteDance Inc.); Liu Jiang (ByteDance Inc.); Jianfei Cui (ByteDance Inc.); Zhichen Zhao (ByteDance Inc.); Xingyan Bin (ByteDance Inc.); Feng Zhang (ByteDance Inc.); Zuotao Liu (ByteDance Inc.)

Tencent
腾讯的工作也比较丰富，涵盖多模态推荐、排序目标引入推荐系统(2篇)、序列建模、兴趣建模、LTV、uplift等。

多模态推荐 | Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback

Guipeng Xv (School of Informatics, Xiamen University); Xinyu Li (School of Informatics, Xiamen University); Ruobing Xie (Tencent); Chen Lin (School of Informatics, Xiamen University); Chong Liu (Tencent); Feng Xia (Tencent); Zhanhui Kang (Tencent); Leyu Lin (Tencent)

排序与校准联合建模 | Beyond Binary Preference: Leveraging Bayesian Approaches for Joint Optimization of Ranking and Calibration

Chang Liu (Shanghai Jiaotong University); Qiwei Wang (Tencent); Wenqing Lin (Tencent); Yue Ding (Shanghai Jiao Tong University); Hongtao Lu (Shanghai Jiao Tong University)

排序目标损失函数理解 | Understanding the Ranking Loss for Recommendation with Sparse User Feedback

Zhutian Lin (Shenzhen International Graduate School, Tsinghua University); Junwei Pan (Tencent Inc.); Shangyu Zhang (Tencent Inc.); Ximei Wang (Tencent Inc.); Xi Xiao (Shenzhen International Graduate School, Tsinghua University); Huang Shudong (Tencent Inc.); Lei Xiao (Tencent Inc.); Jie Jiang (Tencent Inc.)

全生命周期跨场景序列建模 | Cross-Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction

Ruijie Hou (Wechat, Tencent); Zhaoyang Yang (Wechat, Tencent); Yu Ming (Wechat, Tencent); Hongyu Lu (Wechat, Tencent); Zhuobin Zheng (Wechat, Tencent); Yu Chen (Wechat, Tencent); Qinsong Zeng (Wechat, Tencent); Ming Chen (Wechat, Tencent)

动态兴趣建模 | Know in
Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest

Xiaoyu Wang (University of Science and Technology of China, National Institute of Informatics); Yonghui Guo (Tencent Advertising); Hui Sheng (Tencent Advertising); Peili Lv (Tencent Advertising); Chi Zhou (Tencent Advertising); Wei Huang (Tencent Advertising); Shiqin Ta (Tencent Advertising); Dongbo Huang (Tencent Advertising); Xiujin Yang (Tencent Advertising); Lan Xu (Tencent Advertising); Hao Zhou (LINKE Lab, School of Computer Science and Technology, University of Science and Technology of China, CAS Key Laboratory of Wireless-Optical Communications, University of Science and Technology of China); Yusheng Ji (Information Systems Architecture Science Research Division, National Institute of Informatics)

LTV | ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising

Ruize Wang (Tencent Inc.); Hui Xu (Tencent Inc.); Ying Cheng (School of Computer Science, Fudan University); Qi He (Tencent Inc.); Xing Zhou (Tencent Inc.); Rui Feng (School of Computer Science, Fudan University); Wei Xu (Tencent Inc.); Lei Huang (Tencent Inc.); Jie Jiang (Tencent Inc.)

游戏皮肤推荐 | Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model

Yanjie Gou (Common Data Platform, Tencent); Yuanzhou Yao (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Zhao Zhang (Institute of Computing Technology, Chinese Academy of Sciences); Yiqing Wu (Institute of Computing Technology, Chinese Academy of Sciences); Yi Hu (Common Data Platform, Tencent); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, Zhongguancun Laboratory); Jiangming Liu (School of Information Science and Engineering, Yunnan University); Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences)

排序增强的uplift | Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing

Bowei He (City University of Hong Kong); Yunpeng Weng (FiT, Tencent); Xing Tang (FiT, Tencent); Ziqiang Cui (City University of Hong Kong); Zexu Sun (Renmin University of China); Liang Chen (FiT, Tencent); Xiuqiang He (FiT, Tencent); Chen Ma (City University of Hong Kong)

网页搜索 | Enhancing Asymmetric Web Search through Question-Answer Generation and Ranking

Dezhi Ye (Tencent PCG); Jie Liu (Tencent PCG); Jiabin Fan (Tencent PCG); Bowen Tian (Tencent PCG); Tianhua Zhou (Tencent PCG); Xiang Chen (Tencent PCG); Jin Ma (Tencent PCG)

Kuaishou
快手的研究涉及较多融合、重排、校准方面的工作，以及更高阶的用户留存建模等，相比于传统的研究会更吸引注意力。

融合模型 | Unsupervised Ranking Ensemble Model for Recommendation

Wenhui Yu (Kuaishou Technology); Bingqi Liu (Kuaishou Technology); Bin Xia (Kuaishou Technology); Xiaoxiao Xu (Kuaishou Technology); Ying Chen (Kuaishou Technology); Yongchang Li (Kuaishou Technology); Lantao Hu (Kuaishou Technology)

直播礼物推荐 | MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion

Jiaxin Deng (Institute of Automation, School of Artificial Intelligence, University of Chinese Academy of Sciences); Shiyao Wang (KuaiShou Inc.); Yuchen Wang (KuaiShou Inc.); Jiansong Qi (KuaiShou Inc.); Liqin Zhao (KuaiShou Inc.); Guorui Zhou (KuaiShou Inc.); Gaofeng Meng (Institute of Automation)

上下文蒸馏-多样性推荐 | Contextual Distillation Model for Diversified Recommendation

Fan Li (University of Science and Technology of China); Xu Si (Tsinghua University); Shisong Tang (Kuaishou Inc., Tsinghua University); Dingmin Wang (University of Oxford); Kunyan Han (Kuaishou Inc.); Bing Han (Kuaishou Inc.); Guorui Zhou (Kuaishou Inc.); Yang Song (Kuaishou Inc.); Hechang Chen (Jilin University)

用户留存建模 | Modeling User Retention through Generative Flow Networks

Ziru Liu (City University of Hong Kong); Shuchang Liu (Kuaishou Technology); Bin Yang (Kuaishou Technology); Zhenghai Xue (Nanyang Technological University); Qingpeng Cai (Kuaishou Technology); Xiangyu Zhao (City University of Hong Kong); Zijian Zhang (City University of Hong Kong); Lantao Hu (Kuaishou Technology); Han Li (Kuaishou Technology); Peng Jiang (Kuaishou Technology)

非自回归生成的重排模型 | Non-autoregressive Generative Models for Reranking Recommendation

Yuxin Ren (Kuaishou Technology); Qiya Yang (Peking University); Yichun Wu (Tsinghua University); Wei Xu (Kuaishou Technology); Yalong Wang (Kuaishou Technology); Zhiqiang Zhang (Kuaishou Technology)

校准排序 | A Self-boosted Framework for Calibrated Ranking

Shunyu Zhang (Kuaishou Technology); Hu Liu (Kuaishou Technology); Wentian Bao (Columbia University); Yun Yu (Northeasten University); Yang Song (Kuaishou Technology)

Meituan
搜推联合 | Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation

Yuting Zhang (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Yiqing Wu (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Ruidong Han (Meituan); Ying Sun (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)); Yongchun Zhu (Institute of Computing Technology, Chinese Academy of Sciences); Xiang Li (Meituan); Wei Lin (Meituan); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, Zhongguancun Laboratory); Zhulin An (Institute of Computing Technology, Chinese Academy of Sciences); Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences)

拍卖 | Joint Auction in the Online Advertising Market

Zhen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Weian Li (School of Software, Shandong University); Yahui Lei (Meituan Inc.); Bingzhe Wang (Gaoling School of Artificial Intelligence, Renmin University of China); Zhicheng Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Qi Qi (Gaoling School of Artificial Intelligence, Renmin University of China); Qiang Liu (Meituan Inc.); Xingxing Wang (Meituan Inc.)

因果推断 | Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization

Hao Zhou (State Key Laboratory for Novel Software Technology, Nanjing University, Meituan); Rongxiao Huang (State Key Laboratory for Novel Software Technology, Nanjing University); Shaoming Li (Meituan); Guibin Jiang (Meituan); Jiaqi Zheng (State Key Laboratory for Novel Software Technology, Nanjing University); Bing Cheng (Meituan); Wei Lin (Meituan)

JD
电商异构图 | Paths2Pair: Meta-path Based Link Prediction in Billion-Scale Commercial Heterogeneous Graphs

Jinquan Hang (JD Logistics, Rutgers University, New Brunswick); Zhiqing Hong (Rutgers University, New Brunswick); Xinyue Feng (Rutgers University, New Brunswick); Guang Wang (Florida State University); Guang Yang (Rutgers University, New Brunswick); Feng Li (JD Logistics); Xining Song (JD Logistics); Desheng Zhang (Rutgers University, New Brunswick

多任务 | Multi-task Conditional Attention Network for Conversion Prediction in Logistics Advertising

Baoshen Guo (Southeast University, JD Logistics); Xining Song (JD Logistics); Shuai Wang (Southeast University); Wei Gong (University of Science and Technology of China); Tian He (JD Logistics); Xue Liu (McGill University)

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Huawei
华为整体的工作也比较丰富，包括：序列推荐、生成式推荐、生成式内容、时长纠偏、结构化和语义表征、蒸馏、特征选择等。也值得学习下。

序列推荐 | Dataset Regeneration for Sequential Recommendation（最佳学生论文）

Mingjia Yin (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Hao Wang (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Wei Guo (Huawei Singapore Research Center); Yong Liu (Huawei Singapore Research Center); Suojuan Zhang (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Sirui Zhao (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Defu Lian (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Enhong Chen (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence)

生成式推荐 | EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration

Ye Wang (Zhejiang University); Jiahao Xun (Zhejiang University); Minjie Hong (Zhejiang University); Jieming Zhu (Huawei Noah’s Ark Lab); Tao Jin (Zhejiang University); Lin Wang (Zhejiang University); Haoyuan Li (Zhejiang University); Linjun Li (Zhejiang University); Yan Xia (Zhejiang University); Zhou Zhao (Zhejiang University); Zhenhua Dong (Huawei Noah’s Ark Lab)

LLM内容生成 | Neural Retrievers are Biased Towards LLM-Generated Content

Sunhao Dai (Gaoling School of Artificial Intelligence, Renmin University of China); Yuqi Zhou (Gaoling School of Artificial Intelligence, Renmin University of China); Liang Pang (CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences); Weihao Liu (Gaoling School of Artificial Intelligence, Renmin University of China); Xiaolin Hu (Gaoling School of Artificial Intelligence, Renmin University of China); Yong Liu (Gaoling School of Artificial Intelligence, Renmin University of China); Xiao Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Gang Wang (Noah’s Ark Lab, Huawei); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China)

时长纠偏 | Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time

Haiyuan Zhao (School of Information, Renmin University of China); Guohao Cai (Noah’s Ark Lab, Huawei); Jieming Zhu (Noah’s Ark Lab, Huawei); Zhenhua Dong (Noah’s Ark Lab, Huawei); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China); Ji-Rong Wen (Gaoling School of Artificial Intelligence, Renmin University of China)

结构化和语义解耦和协同 | DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation

Kounianhua Du (Shanghai Jiao Tong University); Jizheng Chen (Shanghai Jiaotong University); Jianghao Lin (Shanghai Jiaotong University); Yunjia Xi (Shanghai Jiaotong University); Hangyu Wang (Shanghai Jiao Tong University); Xinyi Dai (Huawei Noah’s Ark Lab); Bo Chen (Huawei Noah’s Ark Lab); Ruiming Tang (Huawei Noah’s Ark Lab); Weinan Zhang (Shanghai Jiao Tong University)

面向RL推荐的优势知识状态蒸馏 | Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation

Qingyao Li (Shanghai Jiao Tong University); Wei Xia (Huawei Noah’s Ark Lab); Li’ang Yin (Shanghai Jiao Tong University); Jiarui Jin (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University)

深度模型特征选择 | ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

Pengyue Jia (City University of Hong Kong); Yejing Wang (City University of Hong Kong); Zhaocheng Du (Huawei Noah’s Ark Lab); Xiangyu Zhao (City University of Hong Kong); Yichao Wang (Huawei Noah’s Ark Lab); Bo Chen (Huawei Noah’s Ark Lab); Wanyu Wang (City University of Hong Kong); Huifeng Guo (Huawei Noah’s Ark Lab); Ruiming Tang (Huawei Noah’s Ark Lab)

Google
多任务 | Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity

Dongyue Li (Northeastern University); Aneesh Sharma (Google); Hongyang R. Zhang (Northeastern University)

多任务探索 | Multi-Task Neural Linear Bandit for Exploration in Recommender Systems

Yi Su (Google Deepmind); Haokai Lu (Google Deepmind); Yuening Li (Google); Liang Liu (Google); Shuchao Bi (Google); Ed H. Chi (Google Deepmind); Minmin Chen (Google Deepmind)

LLM用于拍卖 | Auctions with LLM Summaries

Avinava Dubey (Google Research); Zhe Feng (Google Research); Rahul Kidambi (Google Research); Aranyak Mehta (Google Research); Di Wang (Google Research)

基于生成式AI的评估 | Reliable Confidence Intervals for Information Retrieval Evaluation using Generative A.I.

Harrie Oosterhuis (Google Research, Radboud University); Rolf Jagerman (Google Research); Zhen Qin (Google Research); Xuanhui Wang (Google Research); Michael Bendersky (Google Research)

用户价值 | User Welfare Optimization in Recommender Systems with Competing Content Creators

Fan Yao (University of Virginia); Yiming Liao (Meta Platforms, Inc.); Mingzhe Wu (University of Southern California); Chuanhao Li (Yale University); Yan Zhu (Google); James Yang (Meta Platforms, Inc.); Jingzhou Liu (Meta Platforms, Inc.); Qifan Wang (Meta Platforms, Inc.); Haifeng Xu (University of Chicago); Hongning Wang (University of Virginia)

序列推荐 | Probabilistic Attention for Sequential Recommendation

Yuli Liu (Qinghai University, Qinghai Provincial Key Laboratory of Media Integration Technology and Communication); Christian Walder (Google Research, Brain Team); Lexing Xie (Australian National University, Data61 CSIRO); Yiqun Liu (Department of Computer Science and Technology, Tsinghua University, Zhongguancun Laboratory)‍

Airbnb
airbnb一如既往，标题简短、实战经验满满，也值得关注下。

基于模型蒸馏的多目标学习 | Multi-objective Learning to Rank by Model Distillation

Jie Tang (Airbnb); Huiji Gao (Airbnb); Liwei He (Airbnb); Sanjeev Katariya (Airbnb)

地图场景下的LTR | Learning to Rank for Maps at Airbnb

Malay Haldar (Airbnb, Inc.); Hongwei Zhang (Airbnb, Inc.); Kedar Bellare (Airbnb, Inc.); Sherry Chen (Airbnb, Inc.); Soumyadip Banerjee (Airbnb, Inc.); Xiaotang Wang (Airbnb, Inc.); Mustafa Abdool (Airbnb, Inc.); Huiji Gao (Airbnb, Inc.); Pavan Tapadia (Airbnb, Inc.); Liwei He (Airbnb, Inc.); Sanjeev Katariya (Airbnb, Inc.)

Microsoft
使用LLM和强化学习的topK推荐 | Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning

Amit Sharma (Microsoft Research); Hua Li (Microsoft Bing Ads); Xue Li (Microsoft Bing Ads); Jian Jiao (Microsoft Bing Ads)

使用LLM用于可解释推荐 | RecExplainer: Aligning Large Language Models for Explaining Recommendation Models

Yuxuan Lei (University of Science and Technology of China); Jianxun Lian (Microsoft Research Asia); Jing Yao (Microsoft Research Asia); Xu Huang (University of Science and Technology of China); Defu Lian (University of Science and Technology of China); Xing Xie (Microsoft Research Asia)

检索 | Extreme Meta-classification for Large-Scale Zero-Shot Retrieval

Sachin Yadav (Microsoft Research); Deepak Saini (Microsoft); Anirudh Buvanesh (Microsoft Research); Bhawna Paliwal (Microsoft Research); Kunal Dahiya (Indian Institute of Technology Delhi); Siddarth Asokan (Microsoft Research); Yashoteja Prabhu (Microsoft Research); Jian Jiao (Microsoft); Manik Varma (Microsoft Research)

Shopee
广告校准 | Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising

Shuai Yang (Shopee Discovery Ads); Hao Yang (Shopee Discovery Ads); Zhuang Zou (Shopee Discovery Ads); Linhe Xu (Shopee Discovery Ads); Shuo Yuan (Shopee Discovery Ads); Yifan Zeng (Shopee Discovery Ads)

多目标残差学习 | Residual Multi-Task Learner for Applied Ranking

Cong Fu (Shopee Pte. Ltd.); Kun Wang (Shopee Pte. Ltd.); Jiahua Wu (Shopee Pte. Ltd.); Yizhou Chen (Shopee Pte. Ltd.); Guangda Huzhang (Shopee Pte. Ltd.); Yabo Ni (Nanyang Technological University); Anxiang Zeng (SCSE, National Technological University); Zhiming Zhou (ECONCS, Shanghai University of Finance and Economics)

Meta
离线强化学习出价 | Offline Reinforcement Learning for Optimizing Production Bidding Policies

Dmytro Korenkevych (AI at Meta); Frank Cheng (AI at Meta); Artsiom Balakir (AI at Meta); Alex Nikulkov (AI at Meta); Lingnan Gao (Meta Platform Inc.); Zhihao Cen (AI at Meta); Zuobing Xu (Meta Platform Inc.); Zheqing Zhu (AI at Meta)

多阶段个性化推荐 | Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization

Ariel Evnine (Meta); Stratis Ioannidis (Northeastern University); Dimitris Kalimeris (Meta); Shankar Kalyanaraman (Meta); Weiwei Li (Meta); Israel Nir (Meta); Wei Sun (Meta); Udi Weinsberg (Meta)

冷启动 | Inductive Modeling for Realtime Cold Start Recommendations

Chandler Zuo (Meta); Jonathan Castaldo (Meta); Hanqing Zhu (Meta); Haoyu Zhang (Meta); Ji Liu (Meta); Yangpeng Ou (Meta); Xiao Kong (Meta)

用户福利优化 | User Welfare Optimization in Recommender Systems with Competing Content Creators

Fan Yao (University of Virginia); Yiming Liao (Meta Platforms, Inc.); Mingzhe Wu (University of Southern California); Chuanhao Li (Yale University); Yan Zhu (Google); James Yang (Meta Platforms, Inc.); Jingzhou Liu (Meta Platforms, Inc.); Qifan Wang (Meta Platforms, Inc.); Haifeng Xu (University of Chicago); Hongning Wang (University of Virginia)

Walmart
大模型生成个性化广告 | Chaining Text-to-Image and Large Language Model: A Novel Approach for Generating Personalized e-commerce Banners

Shanu Vashishtha (Walmart Global Tech); Abhinav Prakash (Walmart Global Tech); Lalitesh Morishetti (Walmart Global Tech); Kaushiki Nag (Walmart Global Tech); Yokila Arora (Walmart Global Tech); Sushant Kumar (Walmart Global Tech); Kannan

Amazon
多模态视觉搜索 | Bringing Multimodality to Amazon Visual Search System

Xinliang Zhu (Amazon); Sheng-Wei Huang (Amazon); Han Ding (Amazon); Jinyu Yang (Amazon); Kelvin Chen (Amazon); Tao Zhou (Amazon); Tal Neiman (Amazon); Ouye Xie (Amazon); Son Tran (Amazon); Benjamin Yao (Amazon); Douglas Gray (Amazon); Anuj Bindal (Amazon); Arnab Dhua (Amazon)

统一图学习 | GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications

Da Zheng (Amazon AWS AI); Xiang Song (Amazon AWS AI); Qi Zhu (Amazon AWS AI); Jian Zhang (Amazon AWS AI); Theodore Vasiloudis (Amazon AWS AI); Runjie Ma (Amazon AWS AI); Houyu Zhang (Amazon Search AI); Zichen Wang (Amazon AWS AI); Soji Adeshina (Amazon AWS AI); Israt Nisa (Amazon AWS AI); Alejandro Mottini (Amazon Search AI); Qingjun Cui (Amazon Search AI); Huzefa Rangwala (Amazon AWS AI); Belinda Zeng (Amazon SP); Christos Faloutsos (Amazon AWS AI); George Karypis (Amazon AWS AI)

购物轨迹表示学习 | Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation

Yankai Chen (Department of Computer Science and Engineering, The Chinese University of Hong Kong); Quoc-Tuan Truong (Amazon); Xin Shen (Amazon); Jin Li (Amazon); Irwin King (Department of Computer Science and Engineering, The Chinese University of Hong Kong)

LinkedIn
GNN | LiGNN: Graph Neural Networks at LinkedIn

Fedor Borisyuk (LinkedIn); Shihai He (LinkedIn); Yunbo Ouyang (LinkedIn); Morteza Ramezani (LinkedIn); Peng Du (LinkedIn); Xiaochen Hou (LinkedIn); Chengming Jiang (LinkedIn); Nitin Pasumarthy (LinkedIn); Priya Bannur (LinkedIn); Birjodh Tiwana (LinkedIn); Ping Liu (LinkedIn); Siddharth Dangi (LinkedIn); Daqi Sun (LinkedIn); Zhoutao Pei (LinkedIn); Xiao Shi (LinkedIn); Sirou Zhu (LinkedIn); Qianqi Shen (LinkedIn); Kuang-Hsuan Lee (LinkedIn); David Stein (LinkedIn); Baolei Li (LinkedIn); Haichao Wei (LinkedIn); Amol Ghoting (LinkedIn); Souvik Ghosh (LinkedIn)

元学习 | LiMAML: Personalization of Deep Recommender Models via Meta Learning

Ruofan Wang (LinkedIn Corporation); Prakruthi Prabhakar (LinkedIn Corporation); Gaurav Srivastava (LinkedIn Corporation); Tianqi Wang (LinkedIn Corporation); Zeinab S. Jalali (LinkedIn Corporation); Varun Bharill (LinkedIn Corporation); Yunbo Ouyang (LinkedIn Corporation); Aastha Nigam (LinkedIn Corporation); Divya Venugopalan (LinkedIn Corporation); Aman Gupta (LinkedIn Corporation); Fedor Borisyuk (LinkedIn Corporation); Sathiya Keerthi (LinkedIn Corporation); Ajith Muralidharan (Aliveo AI Corp)

Instacart
电商搜索纠偏 | Mitigating Pooling Bias in E-commerce Search via False Negative Estimation

Xiaochen Wang (Pennsylvania State University); Xiao Xiao (Instacart); Ruhan Zhang (Instacart); Xuan Zhang (Instacart); Taesik Na (Instacart); Tejaswi Tenneti (Instacart); Haixun Wang (Instacart); Fenglong Ma (Pennsylvania State University)

Adobe
LLM用于长尾推荐 | CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation

Junda Wu (University of California San Diego); Cheng-Chun Chang (Columbia University); Tong Yu (Adobe Research); Zhankui He (University of California San Diego); Jianing Wang (University of California San Diego); Yupeng Hou (University of California San Diego); Julian McAuley (University of California San Diego)

RecSys 2024

参考：https://zhuanlan.zhihu/p/714395515

[1] A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
多模态冷启动短视频推荐框架

[2] A Multimodal Single-branch Embedding Network for Recommendation in Cold-start and Missing Modality Scenarios
冷启动和缺失模态场景下的多模态单分支嵌入网络推荐

[3] A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics
基于流行度动态的预训练零样本顺序推荐框架

[4] A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
统一图变换器克服多模态推荐中的隔离问题

[5] Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
加速针对推荐系统的投毒攻击的替代模型重训练

[6] Adaptive Fusion of Multi-View for Graph Contrastive Recommendation
用于图对比推荐的自适应多视图融合

[7] AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
在线广告中用于CTR预测的拍卖信息增强框架

[8] Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
基于LLM的贝叶斯优化用于自然语言偏好引出

[9] Biased User History Synthesis for Personalized Long-Tail Item Recommendation
个性化长尾物品推荐的有偏用户历史合成

[10] Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
在生成式检索中桥接搜索和推荐：一个任务是否帮助另一个？

[11] CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation
CALRec：用于顺序推荐的生成性LLM的对比对齐

[12] ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
ConFit：使用数据增强和对比学习改进简历-工作匹配

[13] Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
通过隐式矩阵分解共享跨域潜在因素

[14] Discerning Canonical User Representation for Cross-Domain Recommendation
为跨域推荐识别规范的用户表示

[15] Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models
蒸馏重要性：使顺序推荐器的性能与大型语言模型相匹配

[16] SeCor: Aligning Semantic and Collaborative representations by Large Language Models for Next-Point-of-Interest Recommendations
SeCor：通过大型语言模型对下一个兴趣点推荐的语义和协同表示进行对齐

[17] DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
DNS-Rec：面向推荐系统的感知数据的神经架构搜索

[18] Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation
动态阶段感知的用户兴趣学习用于异构顺序推荐

[19] Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark
使用EMBark优化嵌入以训练大规模深度学习推荐系统

[20] End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
在预算约束下具有提升建模的端到端成本效益激励推荐

[21] Fair Reciprocal Recommendation in Matching Markets
匹配市场中的公平互惠推荐

[22] FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems
FairCRS：走向对话推荐系统中的用户导向公平性

[23] FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations
FedLoCA：用于联邦推荐的低秩协调适应与知识解耦

[24] FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
FLIP：基于ID的模型与预训练语言模型之间细粒度对齐用于CTR预测

[25] Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial Training
通过跨域分布对抗训练提高推荐模型的对抗鲁棒性

[26] Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
提升最短板：面向鲁棒推荐系统的漏洞感知对抗训练

[27] Information-Controllable Graph Contrastive Learning for Recommendation
用于推荐的可控信息图对比学习

[28] Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations
指导和提示大型语言模型以实现可解释的跨域推荐

[29] LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
LARR：大型语言模型辅助的实时场景推荐与语义理解

[30] Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation
低秩场加权分解机用于低延迟物品推荐

[31] MARec: Metadata Alignment for cold-start Recommendation
MARec：元数据对齐用于冷启动推荐

[32] MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
MLoRA：多域低秩自适应网络用于CTR预测

[33] MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations
MMGCL：元知识增强的多视图图对比学习用于推荐

[34] Multi-Objective Recommendation via Multivariate Policy Learning
通过多变量策略学习的多目标推荐

[35] Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
并非所有视频都会过时：通过学习消除发布间隔偏见的短视频推荐

[36] Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
重新思考推荐系统中候选匹配的多兴趣学习

[37] Only a Controllable Reasoning Pool is Enough! Effortlessly Integrating Large Language Models’ Insights into Industrial Recommenders
只有一个可控的推理池就够了！轻松将大型语言模型的洞察力整合到工业推荐器中

[38] Optimal Baseline Corrections for Off-Policy Contextual Bandits
离策略上下文强盗的最优基线校正

[39] Prompt Tuning for Item Cold-start Recommendation
面向物品冷启动推荐的提示调整

[40] Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders
测试流行偏见缓解：在音乐推荐器中的用户中心评估

[41] Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space
通过在整个曝光空间上的AUC优化的排名意识无偏后点击转化率估计

[42] Repeated Padding for Sequential Recommendation
顺序推荐的重复填充

[43] Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery
正确的工具，正确的工作：食品配送中的重复和探索消费推荐

[44] RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
分配框架

[45] Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
适用于大型项目目录的顺序推荐的可扩展交叉熵损失

[46] Scaling Law of Large Sequential Recommendation Models
大型顺序推荐模型的扩展法则

[47] Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
CTR预测中动态冷启动场景优化的场景自适应网络

[48] The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
房间里的大象：重新思考预训练语言模型在顺序推荐中的使用

[49] The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
我们推荐的问题：关于优化可衡量指标的危险

[50] The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic View
一种概率视角

[51] Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
触及核心：探索推荐中混合目标之间的任务依赖

[52] Towards Empathetic Conversational Recommender Systems
走向富有同情心的对话推荐系统

[53] Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
利用大型语言模型的知识增强实现开放世界推荐

[54] Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
R：重复意识和顺序听歌会话推荐

[55] Unified Denoising Training for Recommendation
推荐系统的统一去噪训练

[56] Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender Systems
在对话推荐系统中释放大型语言模型的检索潜力

[57] Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
解锁隐藏的宝藏：用未标记数据增强推荐

[58] Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction
利用半监督学习利用非点击样本进行转化率预测

SIGIR 2024

转载自： https://zhuanlan.zhihu/p/695601620

Title: ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social NetworksAuthor(s): Md Asaduzzaman Noor, Jason A. Clark, John Sheppard
标题：ScholarNodes：应用基于内容的过滤来推荐学术社交网络中的跨学科社区作者：Md Asaduzzaman Noor、Jason A. Clark、John Sheppard

Title: MACRec: A Multi-Agent Collaboration Framework for RecommendationAuthor(s): Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang
用于推荐的多代理协作框架

Title: Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State TrackingAuthor(s): Justin Cui, Kai Dicarlantonio, Sara Kemper, Kathy Lin, Danjie Tang, Anton Korikov, Scott Sanner
标题：基于提示的半结构化自然语言状态跟踪的检索增强会话推荐作者：Justin Cui, Kai Dicarlantonio, Sara Kemper, Kathy Lin, Danjie Tang, Anton Korikov, Scott Sanner

Title: A Generic Behavior-Aware Data Augmentation Framework for Sequential RecommendationAuthor(s): Jing Xiao, Weike Pan, Zhong Ming
标题：用于顺序推荐的通用行为感知数据增强框架作者：Jing Xiao、Weike Pan、Zhong Ming

Title: Large Language Models are Learnable Planners for Long-Term RecommendationAuthor(s): Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng
大语言模型是用于长期推荐的可学习规划器

Title: LoRec: Combating Poisons with Large Language Model for Robust Sequential RecommendationAuthor(s): Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng
标题：LoRec：用大型语言模型对抗毒药以实现稳健的顺序推荐作者：Kaike Zhang、Qi Cao、Yunfan Wu、Fei Sun、Huawei Shen、Xueqi Cheng

Title: Multi-Domain Sequential Recommendation via Domain Space LearningAuthor(s): Junyoung Hwang, Hyunjun Ju, SeongKu Kang, Sanghwan Jang, Hwanjo Yu
通过域空间学习进行多域序列推荐

Title: MDMTRec: An Adaptive Multi-Task Multi-Domain Recommendation FrameworkAuthor(s): Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai
自适应多任务多领域推荐框架

Title: Enhancing Session Recommendations: Unveiling User Intent with Large Language ModelsAuthor(s): Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, Yew Soon Ong
增强会话推荐： 利用大型语言模型揭示用户意图

Title: LLaRA: Large Language-Recommendation Assistant Author(s): Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He
LLaRA：大型语言推荐助手

Title: Configurable Fairness for New Item Recommendation Considering Entry Time of ItemsAuthor(s): Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Li, Jie Zhang
考虑项目输入时间的可配置新项目推荐公平性

Title: Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationAuthor(s): Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, Yahong Han
针对多行为推荐的行为-情境项目偏好建模

Title: DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group RecommendationAuthor(s): Yingqi Zhao, Haiwei Zhang, Qijie Bai, Changli Nie, Xiaojie Yuan
DHMAE：用于群体推荐的分解超图掩码自动编码器

Title: Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation SystemsAuthor(s): Yang Li, Qi’Ao Zhao, Chen Lin, Jinsong Su, Zhilin Zhang
标题：与谁对齐：推荐系统中面向反馈的多模态对齐作者：Yang Li、Qi’Ao Zhao、Chen Lin、Jinsong Su、Zhilin Zhang

Title: Fair Sequential Recommendation without User DemographicsAuthor(s): Huimin Zeng, Zhankui He, Zhenrui Yue, Julian McAuley, Dong Wang
无用户特征的公平顺序推荐

Title: Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information AlignmentAuthor(s): Xinyu Zhu, Lilin Zhang, Ning Yang
标题：通过信息对齐实现个性化公平推荐的自适应公平表示学习作者：Xinyu Zhu、Lilin 张、Ning Yang

Title: Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential RecommendationAuthor(s): Chung Park, Taesan Kim, Hyungjun Yoon, Junui Hong, Yelim Yu, Mincheol Cho, Minsung Choi, Jaegul Choo
标题：Pacer 和 Runner：单域和跨域顺序推荐之间的合作学习框架作者：Chung Park, Taesan Kim, Hyungjun Yoon, Junui Hong, Yelim Yu, Mincheol Cho, Minsung Choi, Jaegul Choo

Title: Aiming at the Target: Filter Collaborative Information for Cross-Domain RecommendationAuthor(s): Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma
瞄准目标： 为跨域推荐过滤协作信息

Title: Reinforcing Long-Term Performance in Recommender Systems with User-Oriented Exploration PolicyAuthor(s): Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu
利用面向用户的探索策略强化推荐系统的长期性能

Title: Self-Augmented Graph Neural Networks for Sequential RecommendationAuthor(s): ? ??, Lianghao Xia, Chao Huang
标题：用于顺序推荐的自增强图神经网络作者：？ ??、夏良浩、黄超

Title: Diffusion Models for Generative Outfit RecommendationAuthor(s): Yiyan Xu, Wang Wenjie, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He
用于生成式服装推荐的扩散模型

Title: GPT4Rec: Graph Prompt Tuning for Streaming RecommendationAuthor(s): Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Liying Kang, Xi Zhang, Feiran Huang, Senzhang Wang, Sunghun Kim
GPT4Rec： 用于流媒体推荐的图提示调整

Title: TransGNN: Harnessing the Collaborative Power of Transformer and Graph Neural Network for Recommender SystemsAuthor(s): Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Xi Zhang, Senzhang Wang, Feiran Huang, Sunghun Kim
标题：TransGNN：利用 Transformer 和图神经网络的协作能力进行推荐系统作者：Peiyan Zhang、Yuchen Yan、Chaozhuo Li、Xi Zhang、Senzhang Wang、Feiran Huang、Sunghun Kim

Title: EditKG: Editing Knowledge Graph for RecommendationAuthor(s): Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, Chenghu Zhou
EditKG：用于推荐的知识图谱编辑

Title: AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsAuthor(s): Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
AFDGCF：用于推荐的自适应特征去相关图协同过滤

Title: IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTAuthor(s): Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose
标题：IISAN：通过解耦 PEFT 有效调整多模态表示以实现顺序推荐作者：Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose

Title: Disentangled Contrastive Hypergraph Learning for Next POI RecommendationAuthor(s): Yantong Lai, Yijun Su, Lingwei Wei, Tianqi He, Haitao Wang, Gaode Chen, Daren Zha, Qiang Liu, Xingxing Wang
用于下一个 POI 推荐的离散对比超图学习

Title: CLLP: Contrastive Learning framework based on Latent Preferences for next POI recommendationAuthor(s): Hongli Zhou, Zhihao Jia, Haiyang Zhu, Zhizheng Zhang
标题：CLLP：基于潜在偏好的对比学习框架用于下一个 POI 推荐作者：周红利、贾志浩、朱海洋、张志正

Title: To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes ProcessAuthor(s): Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang, Yang Song, Hongteng Xu, Jun Xu

Title: On the Negative Perception of Cross-domain Recommendations and ExplanationsAuthor(s): Denis Kotkov, Alan Medlar, Yang Liu, Dorota Glowacka
标题：论跨域建议和解释的负面认知作者：Denis Kotkov、Alan Medlar、Yang Liu、Dorota Glowacka

Title: Treatment Effect Estimation for User Interest Exploration on Recommender SystemsAuthor(s): Jiaju Chen, Wang Wenjie, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua
推荐系统中用户兴趣探索的处理效果估计

Title: Hypergraph Convolutional Network for User-Oriented Fairness in Recommender SystemsAuthor(s): Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Li Zhang, Yuyuan Li
面向用户公平性的超图卷积网络在推荐系统中的应用

Title: SIGformer: Sign-aware Graph Transformer for RecommendationAuthor(s): Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang
SIGformer： 用于推荐的标识感知图转换器

Title: Disentangling ID and Modality Effects for Session-based RecommendationAuthor(s): Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang, Hongfei Lin, Fenglong Ma
基于会话推荐的 ID 和模式效应分离

Title: Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender SystemsAuthor(s): Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke
标题：超越流行度和积极性偏差：纠正推荐系统中的多因素偏差作者：Jin Huang、Harrie Oosterhuis、Masoud Mansoury、Herke van Hoof、Maarten de Rijke

Title: Broadening the View: Demonstration-augmented Prompt Learning for Conversational RecommendationAuthor(s): Huy Quang Quang Dao, Yang Deng, Dung Le, Lizi Liao
标题：拓宽视野：会话推荐的演示增强即时学习作者：Huy Quang Quang Dao、Yang Deng、Dung Le、Lizi Liao

Title: Optimal Transport Enhanced Cross-City Site RecommendationAuthor(s): Xinhang Li, Xiangyu Zhao, Zihao Wang, Yang Duan, Yong Zhang, Chunxiao Xing
最优传输增强型跨城市站点推荐

Title: Identifiability of Cross-Domain Recommendation via Causal Subspace DisentanglementAuthor(s): Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao
域网站的可识别性 通过因果子空间解缠实现跨域推荐的可识别

Title: FineRec: Exploring Fine-grained Sequential RecommendationAuthor(s): Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma
FineRec： 探索细粒度序列推荐

Title: Sequential Recommendation with Latent Relations based on Large Language ModelAuthor(s): Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
标题：基于大语言模型的潜在关系序列推荐作者：Shenghao Yang、Weizhi Ma、Peijie Sun、Qingyao Ai、Yiqun Liu、Mingchen Cai、Min Zhang

Title: ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User BenefitAuthor(s): Wenjie Li, Zhongren Wang, Jinpeng Wang, Shu-Tao Xia, Jile Zhu, Mingjian Chen, Jiangke Fan, Jia Cheng, Jun Lei
ReFer： 检索增强的垂直联合推荐全集用户利益

Title: Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective TransportAuthor(s): Jiajie Su, Chaochao Chen, Weiming Liu, Zibin Lin, Shuheng Shen, Weiqiang Wang, Xiaolin Zheng
联合推荐上的目标模型中毒重访

Title: Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action ModelingAuthor(s): Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon Jose
标题：基于强化学习的推荐系统，具有用于状态奖励和行动建模的大型语言模型作者：Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon Jose

Title: Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationAuthor(s): Yi Yu, Kazunari Sugiyama, Adam Jatowt
标题：通过互信息最大化进行协作解释的顺序推荐作者：Yi Yu, Kazunari Sugiyama, Adam Jatowt

Title: MIRROR: A Multi-View Reciprocal Recommender System for Online RecruitmentAuthor(s): Zhi Zheng, Xiao Hu, Shanshan Gao, Hengshu Zhu, Hui Xiong
用于在线招聘的多视图互惠推荐系统

Title: Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain RecommendationsAuthor(s): Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa
基于互信息的非重叠多目标跨域推荐的偏好分解和转移

Title: Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language ModelsAuthor(s): Yankun Ren, Zhongde Chen, Xinxing Yang, Longfei Li, Cong Jiang, Lei Cheng, Bo Zhang, Linjian Mo, Jun Zhou
利用对齐大型语言模型的增强知识增强序列推荐

Title: Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease PatientsAuthor(s): Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He
不让一个病人掉队 加强罕见病患者的用药推荐

Title: DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain RecommendationAuthor(s): Yu Li, Yi Zhang, Zimu Zhou, Qiang Li
DeCoCDR：面向跨域推荐的可部署云设备协作

Title: CaDRec: Contextualized and Debiased Recommender ModelAuthor(s): Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, Dongjin Yu
CaDRec： 情境化和去偏差推荐模型

Title: Content-based Graph Reconstruction for Cold-start item recommendationAuthor(s): Jinri Kim, Eungi Kim, Kwangeun Yeo, Yujin Jeon, Chanwoo Kim, Sewon Lee, Joonseok Lee
标题：基于内容的图重建冷启动项目推荐作者：Jinri Kim, Eungi Kim, Kwangeun Yeo, Yujin Jeon, Chanwoo Kim, Sewon Lee, Joonseok Lee

Title: Modeling User Fatigue for Sequential RecommendationAuthor(s): Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao
序列推荐的用户疲劳建模

Title: Data-efficient Fine-tuning for LLM-based RecommendationAuthor(s): Xinyu Lin, Wang Wenjie, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, Tat-Seng Chua
基于 LLM 的数据高效微调推荐

Title: Multimodality Invariant Learning for Multimedia-Based New Item RecommendationAuthor(s): Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, Meng Wang
基于多媒体的新项目推荐的多模态不变性学习

Title: NFARec: A Negative Feedback-Aware Recommender ModelAuthor(s): Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Dongjin Yu
NFARec： 具有负反馈意识的推荐模型

Title: UniSAR: Modeling User Transition Behaviors between Search and RecommendationAuthor(s): Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, Yang Song
UniSAR：用户在搜索和推荐之间的转换行为建模

Title: Poisoning Decentralized Collaborative Recommender System and Its CountermeasuresAuthor(s): Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin
去中心化协作推荐系统中毒及其对策

Title: Scaling Sequential Recommendation Models with TransformersAuthor(s): Pablo Zivic, Hernán Vazquez, Jorge Sánchez
标题：使用 Transformers 扩展顺序推荐模型作者：Pablo Zivic、Hernán Vazquez、Jorge Sánchez

Title: AutoDCS: Automated Decision Chain Selection in Deep Recommender SystemsAuthor(s): Dugang Liu, Shenxian Xian, Wu Yuhao, Chaohua Yang, Xing Tang, Xiuqiang He, Zhong Ming
AutoDCS： 深度推荐系统中的自动决策链选择

Title: Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization ApproachAuthor(s): Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He
有限敏感属性下的公平推荐： 一种分布稳健的优化方法

Title: Denoising Diffusion Recommender ModelAuthor(s): Jujia Zhao, Wang Wenjie, Yiyan Xu, Teng Sun, Fuli Feng, Tat-Seng Chua
去噪扩散推荐模型

Title: Let Me Do It For You: Towards LLM Empowered Recommendation via Tool LearningAuthor(s): Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, Maarten De Rijke
让我为你做： 通过工具学习实现 LLM 授权推荐

Title: Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionAuthor(s): Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai
优化即时反馈和长期保留的顺序建议

Title: Can we trust joint evaluation measures of relevance and fairness in recommender systems?Author(s): Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro, Christina Lioma
标题：我们可以信任推荐系统中相关性和公平性的联合评估措施吗？作者：Theresia Veronika Rampisela、Tukka Ruotsalo、Maria Maistro、Christina Lioma

Title: Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?Author(s): Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, Maarten de Rijke
标题：我们真的在下一个篮子推荐中实现更好的超准确性能吗？作者：Ming Li、Yuanna Liu、Sami Jullien、Mozhdeh Ariannezhad、Andrew Yates、Mohammad Aliannejadi、Maarten de Rijke

Title: CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential RecommendationAuthor(s): Xiaolong Xu, Hongsheng Dong, Lianyong Qi, Xuyun Zhang, Haolong Xiang, Xiaoyu Xia, Yanwei Xu, Wanchun Dou
CMCLRec： 针对用户冷启动顺序推荐的跨模态对比学习

Title: Large Language Models for Next Point-of-Interest RecommendationAuthor(s): Peibo Li, Maarten de Rijke, Hao Xue, Shuang Ao, Yang Song, Flora D. Salim
用于下一个兴趣点推荐的大型语言模型

Title: Course Recommender Systems Need to Consider the Job MarketAuthor(s): Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser
标题：课程推荐系统需要考虑就业市场作者：Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser

Title: On Generative Agents in RecommendationAuthor(s): An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, Tat-Seng Chua
关于推荐中的生成代理

Title: SM-RS: Single- and Multi-objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity ScoresAuthor(s): Patrik Dokoupil, Ladislav Peska, Ludovico Boratto
标题：SM-RS：具有上下文印象和超准确倾向评分的单目标和多目标推荐作者：Patrik Dokoupil、Ladislav Peska、Ludovico Boratto

Title: OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender SystemsAuthor(s): Shuyuan Xu, Wenyue Hua, Yongfeng Zhang
标题：OpenP5：用于开发、培训和评估基于 LLM 的推荐系统的开源平台作者：Shuyuan Xu、Wenyue Hua、Yongfeng Zhang

Title: MealRec: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessAuthor(s): Ming Li, Lin Li, Xiaohui Tao, Jimmy Xiangji Huang
标题：MealRec：具有膳食课程隶属关系的个性化和健康膳食推荐数据集作者：Ming Li、Lin Li、Xiaohui Tao、Jimmy Xianji Huang

Title: An Empirical Analysis on Multi-turn Conversational Recommender SystemsAuthor(s): Lu Zhang, Chen Li, Yu Lei, Zhu Sun, Guanfeng Liu
多轮对话推荐系统的实证分析

Title: MIND Your Language: A Multilingual Dataset for Cross-lingual News RecommendationAuthor(s): Andreea Iana, Goran Glavaš, Heiko Paulheim
标题：介意你的语言：跨语言新闻推荐的多语言数据集作者：Andreea Iana、Goran Glavaš、Heiko Paulheim

Title: EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video RecommendationAuthor(s): Shaorun Zhang, Zhiyu He, Ziyi Ye, Peijie Sun, Qingyao Ai, Min Zhang, Yiqun Liu
EEG-SVRec： 短视频推荐中带有用户多维情感参与标签的脑电图数据集

Title: Amazon-KG: A Knowledge Graph Enhanced Cross-Domain Recommendation DatasetAuthor(s): Yuhan Wang, Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, Yongjian Liu
Amazon-KG：知识图谱增强型跨域推荐数据集

Title: Dataset and Models for Item Recommendation Using Multi-Modal User InteractionsAuthor(s): Simone Borg Bruun, Krisztian Balog, Maria Maistro
标题：使用多模式用户交互进行项目推荐的数据集和模型作者：Simone Borg Bruun、Krisztian Balog、Maria Maistro

Title: EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsAuthor(s): Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang
标题：EasyRL4Rec：一个易于使用的基于强化学习的推荐系统库作者：Yuanqing Yu、Chongming Gau、Jiawei Chen、Heng Tang、Yuefeng Sun、Qian Chen、Weizhi Ma、Min Zhang

Title: OpenSiteRec: An Open Dataset for Site RecommendationAuthor(s): Xinhang Li, Xiangyu Zhao, Yejing Wang, Yu Liu, Chong Chen, Cheng Long, Yong Zhang, Chunxiao Xing
OpenSiteRec： 用于网站推荐的开放数据集

Title: ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain RecommendationAuthor(s): Hou Chaoqun, Yuanhang Zhou, Yi Cao, Tong Liu
标题：ECAT：跨域推荐的全空间持续自适应迁移学习框架作者：侯超群、周远航、曹一、刘童

Title: A Unified Search and Recommendation Framework based on Multi-Scenario Learning for Ranking in E-commerceAuthor(s): Jinhan Liu, Qiyu Chen, Junjie Xu, Junjie Li, Baoli Li, Sulong Xu
基于多场景学习的电子商务排名统一搜索和推荐框架

Title: Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation PoliciesAuthor(s): Chih-Wei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier
标题：最小化推荐系统中的实时实验：用户模拟评估偏好诱导策略作者：Chih-Wei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben克雷格·布提利尔·谢茨

Title: Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query ExpansionAuthor(s): Pau Perng-Hwa Kung, Zihao Fan, Tong Zhao, Yozen Liu, Zhixin Lai, Jiahui Shi, Yan Wu, Jun Yu, Neil Shah, Ganesh Venkataraman
利用 ANN 查询扩展改进基于嵌入的好友推荐检索

Title: Monitoring the Evolution of Behavioural Embeddings in Social Media RecommendationAuthor(s): Srijan Saket, Olivier Jeunen, Md. Danish Kalim
标题：监控社交媒体推荐中行为嵌入的演变 作者：Srijan Saket、Olivier Jeunen、Md.丹麦卡利姆

Title: Interest Clock: Time Perception in Real-Time Streaming Recommendation SystemAuthor(s): Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li, Feng Zhang, Zuotao Liu
兴趣时钟： 实时流媒体推荐系统中的时间感知

Title: SLH-BIA: Short-Long Hawkes Process for Buy It Again Recommendations at ScaleAuthor(s): Rankyung Park, Amit Pande, David Relyea, Pushkar Chennu, Prathyusha Kanmanth Reddy
标题：SLH-BIA：大规模再次购买建议的短-长霍克斯流程作者：Rankyung Park、Amit Pande、David Relyea、Pushkar Chennu、Prathyusha Kanmanth Reddy

Title: Analyzing and Mitigating Repetitions in Trip RecommendationAuthor(s): Wenzheng Shu, Kangqi Xu, Wenxin Tai, Ting Zhong, Yong Wang, Fan Zhou
分析和减少行程推荐中的重复现象

Title: Explainable Uncertainty Attribution for Sequential RecommendationAuthor(s): Carles Balsells-Rodas, Fan Yang, Zhishen Huang, Yan Gao
标题：顺序推荐的可解释不确定性归因作者：Carles Balsells-Rodas、Fan Yang、Zhishen Huang、Yan Gau

Title: Cross-reconstructed Augmentation for Dual-target Cross-domain RecommendationAuthor(s): Qingyang Mao, Qi Liu, Zhi Li, Likang Wu, Bing Lv, Zheng Zhang
用于双目标跨域推荐的交叉构建增强技术

Title: Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast RecommendationAuthor(s): Jin-Duk Park, Yong-Min Shin, Won-Yong Shin
标题：Turbo-CF：用于快速推荐的矩阵无分解图过滤作者：Jin-Duk Park、Yong-Min Shin、Won-Yong Shin

Title: MoME: Mixture-of-Masked-Experts for Efficient Multi-Task RecommendationAuthor(s): Jiahui Xu, Lu Sun, Dengji Zhao
标题：MoME：混合蒙面专家的高效多任务推荐作者：Jiahui Xu、Lu Sun、Dengji Zhao

Title: Multi-intent-aware Session-based RecommendationAuthor(s): Minjin Choi, Hye-Young Kim, Hyunsouk Cho, Jongwuk Lee
标题：基于多意图感知的会话推荐作者：Minjin Choi、Hye-Young Kim、Hyunsouk Cho、Jongwuk Lee

Title: Modeling Domains as Distributions with Uncertainty for Cross-Domain RecommendationAuthor(s): Xianghui Zhu, Mengqun Jin, Hengyu Zhang, Chang Meng, Daoxin Zhang, Xiu Li
将域建模为具有不确定性的分布，以实现跨域推荐

Title: Behavior Pattern Mining-based Multi-Behavior RecommendationAuthor(s): Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du
于行为模式挖掘的多行为推荐

Title: Memory-Efficient Deep Recommender Systems using Approximate Rotary Compositional EmbeddingAuthor(s): Dongning Ma, Xun Jiao
标题：使用近似旋转组合嵌入的内存高效深度推荐系统作者：Dongning Ma, Xun Jiao

Title: Masked Graph Transformer for Large-Scale RecommendationAuthor(s): Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh, Vivian Lai, Yan Zheng, Minghua Xu, Hanghang Tong
用于大规模推荐的屏蔽图变换器

Title: Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation SystemsAuthor(s): Dayu Yang, Fumian Chen, Hui Fang
标题：行为调整：评估基于 LLM 的会话推荐系统的新视角作者：Dayu Yang、Fumian Chen、Hui Fang

Title: Multi-Layer Ranking with Large Language Models for News Source RecommendationAuthor(s): Wenjia Zhang, Lin Gui, Rob Procter, Yulan He
标题：新闻源推荐的大型语言模型多层排序作者：Wenjia Zhang, Lin Gui, Rob Procter, Yulan He

Title: Neural Click Models for Recommender SystemsAuthor(s): Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, Andrey Savchenko, Sergey Nikolenko
标题：推荐系统的神经点击模型作者：Mikhail Shirokikh、Ilya Shenbin、Anton Alekseev、Anna Volodkevich、Alexey Vasilev、Andrey Savchenko、Sergey Nikolenko

Title: SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based RecommendationAuthor(s): Muskan Gupta, Priyanka Gupta, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff
标题：SCM4SR：基于结构因果模型的数据增强，实现基于会话的鲁棒推荐作者：Muskan Gupta、Priyanka Gupta、Jyoti Narwariya、Lovekesh Vig、Gautam Shroff

Title: Graph Diffusive Self-Supervised Learning for Social RecommendationAuthor(s): Jiuqiang Li, Hongjun Wang
标题：用于社交推荐的图扩散自监督学习作者：Jiuqiang Li、Hongjun Wang

Title: Self-Explainable Next POI RecommendationAuthor(s): Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, Fan Zhou
可自行解释的下一个 POI 推荐

Title: Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
标题：探索图协同过滤交互背后意图的个体性和集体性
Author(s): Yi Zhang, Lei Sang, Yiwen Zhang

Title: Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
标题：基于扩散模型的协同过滤：揭示高阶连接的潜力
Author(s): Yu Hou, Jin-Duk Park, Won-Yong Shin

Title: Graph Signal Diffusion Model for Collaborative Filtering
Author(s): Yunqin Zhu, Chao Wang, Qi Zhang, Hui Xiong

Title: Lightweight Embeddings for Graph Collaborative Filtering
标题：图协同过滤的轻量级嵌入
Author(s): Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, Hongzhi Yin

Title: Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering
标题：揭露隐私：基于混淆的协同过滤扰动技术的再现和评估研究
Author(s): Alex Martinez, Mihnea Tufis, Ludovico Boratto
作者：Alex Martinez、Mihnea Tufis、Ludovico Boratto

AAAI 2024

1. Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation
   稀疏增强网络：顺序推荐中鲁棒增强的对抗生成方法
2. Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations
   通过群体适应对有偏差的优惠券推荐进行后门调整
3. Temporally and Distributionally Robust Optimization for Cold-start Recommendation
   冷启动建议的时间和分布鲁棒优化
4. Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation
   通过动态嵌入传输学习准确的双向转换以进行跨域推荐
5. Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
   探索在线职位推荐中图数据理解的大语言模型
6. Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
   同伴学习：通过行动建议从头开始学习小组中的复杂政策
7. CoreRec: A Counterfactual Correlation Inference for Next Set Recommendation
   CoreRec：下一组推荐的反事实相关性推理
8. Enhancing Job Recommendation through LLMbased Generative Adversarial Networks
   通过基于法学硕士的生成对抗网络增强工作推荐
9. Temporal Graph Contrastive Learning for Sequential Recommendation
   用于顺序推荐的时间图对比学习
10. Less is More: Label Recommendation for Weakly Supervised Point Cloud Semantic Segmentation
    少即是多：弱监督点云语义分割的标签推荐
11. Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
    在线实验中持续时间推荐的效果大小估计：利用分层模型和客观效用方法
12. D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations
    D3：多领域推荐中的领域划分、建模和平衡的方法论探索
13. STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
    STEM：释放嵌入的力量以实现多任务推荐
14. No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
    没有偏见！用于个性化推荐的公平联合图神经网络
15. Fine-tuning Large Language Model based Explainable Recommendation with Explainable Quality Reward
    微调基于大型语言模型的可解释推荐和可解释质量奖励
16. VITA: ‘Carefully Chosen and Weighted Less’ Is Better in Medication Recommendation
    VITA：“谨慎选择、权重减轻”在药物推荐中效果更好
17. A Goal Interaction Graph Planning Framework for Conversational Recommendation
    一种用于会话推荐的目标交互图规划框架
18. RRL: Recommendation Reverse Learning
    RRL：推荐逆向学习
19. Dual-view Whitening on Pre-trained Text Embeddings for Sequential Recommendation
    用于顺序推荐的预训练文本嵌入的双视图白化
20. Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation
    图解纠缠对比学习与跨域推荐的个性化迁移
21. LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs
    Spectral-based Graph Neutral Networks for Complementary Item Recommendation
    用于补充项目推荐的基于谱的图神经网络
22. LGMRec: Local and Global Graph Learning for Multimodal Recommendation
    LGMRec：用于多模态推荐的本地和全局图学习
23. Plug-in Diffusion Model for Sequential Recommendation
    用于顺序推荐的插件扩散模型
24. Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation
    通过类脑时空感知表示进行后续 POI 推荐
25. Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
    通过领域偏好建模进行多领域推荐吸引用户
26. Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
    Ada-Retrieval：用于顺序推荐的自适应多轮检索范式
27. An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention
    超越自注意力的顺序推荐的注意力归纳偏差
28. Distributional Off-Policy Evaluation for Slate Recommendations
    Slate 建议的分布式离策略评估
29. Review-Enhanced Hierarchical Contrastive Learning for Recommendation
    用于推荐的复习增强分层对比学习
30. Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation
    用于项目冷启动推荐的偏好感知双重对比学习
31. Tail-STEAK: Improve Friend Recommendation for Tail Users via Self-Training Enhanced Knowledge Distillation
    Tail-STEAK：通过自我训练增强知识蒸馏，提高Tail用户的好友推荐
32. Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation
    通过移动树学习时隙偏好以进行下一个 POI 推荐
33. Knowledge-Aware Explanable Reciprocal Recommendation
    知识感知的可解释的相互推荐
34. Adaptive Hardness Negative Sampling for Collaborative Filtering
    用于协同过滤的自适应硬度负采样
35. Generalize for Future: Slow and Fast Trajectory learning for CTR prediction
    面向未来泛化：用于点击率预测的慢速和快速轨迹学习
36. AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
    AT4CTR：用于增强点击率预测的辅助匹配任务

WSDM 2024

转载自： https://zhuanlan.zhihu/p/665023987

推荐
Defense Against Model Extraction Attacks on Recommender Systems（南阳理工）【推荐系统攻防】
Sixiao Zhang (Nanyang Technological University)*; Hongzhi Yin (The University of Queensland); Hongxu Chen (The University of Queensland); Cheng Long (Nanyang Technological University)

Motif-based Prompt Learning for Universal Cross-domain Recommendation（首都师范）【基于Motif的通用跨域推荐提示学习】
Bowen Hao (Captial Normal University)*; Chaoqun Yang (Griffith University); Lei Guo (Shandong Normal University); Junliang Yu (The University of Queesland); Hongzhi Yin (The University of Queensland)

To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders（亚马逊）【复制或不复制；这是神经序列推荐器中输出Softmax层的一个关键问题】
Haw-shiuan Chang (Amazon)*; Nikhil Agarwal (http://Amazon); Andrew McCallum (Univ of Massachusetts Amherst)

Linear Recurrent Units for Sequential Recommendation（伊利诺伊）【序列推荐的线性递归单元】
Zhenrui Yue (University of Illinois Urbana-Champaign); Yueqi Wang (University of California, Berkeley); Zhankui He (UC, San Diego)*; Huimin Zeng (University of Illinois at Urbana-Champaign); Julian McAuley (UCSD); Dong Wang (University of Illinois Urbana-Champaign)

User Behavior Enriched Temporal Knowledge Graph for Sequential Recommendation（新加坡国立，华为）【用户行为丰富知识图谱，用于序列推荐】
Hengchang Hu (National University of Singapore)*; Wei Guo (Huawei Noah’s Ark Lab); Xu Liu (National University of Singapore); Yong Liu (Huawei); Ruiming Tang (Huawei Noah’s Ark Lab); Rui Zhang (http://ruizhang.info); Min-Yen Kan (National University of Singapore)

Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation（东吴大学）【基于跨子序列的意图对比学习序列推荐】
Xiuyuan Qin (Soochow University)*; Huanhuan Yuan (Soochow University); Pengpeng Zhao (Soochow University); Guanfeng Liu (Macquarie University); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University); Victor S. Sheng (Texas Tech University)

Budgeted Embedding Table For Recommender Systems（昆士兰）【推荐系统的嵌入表研究】
Yunke Qu (The University of Queensland)*; Tong Chen (The University of Queensland); Quoc Viet Hung Nguyen (Griffith University); Hongzhi Yin (The University of Queensland)

Pre-trained Recommender Systems: A Causal Debiasing Perspective（威斯康星，亚马逊）【预训练推荐系统：因果去偏的视角】
Ziqian Lin (University of Wisconsin–Madison)*; Hao Ding (AWS AI Lab); Nghia Trong Hoang (Washington State University); Branislav Kveton (AWS AI Labs); Anoop Deoras (Amazon); Hao Wang (Rutgers University)

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation（中科大）【动态稀疏学习：一种高效推荐的新范式】
Shuyao Wang (University of Science and Technology of China); Yongduo Sui (University of Science and Technology of China); Jiancan Wu (University of Science and Technology of China); Zhi Zheng (University of Science and Technology of China); Hui Xiong (Hong Kong University of Science and Tech)
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation（蚂蚁）【PEACE：用于跨域推荐的原型lEarning增强可迁移框架】
Chunjing Gan (Ant Group); Bo Huang (Ant Group); Binbin Hu (Ant Group); Jian Ma (Ant Group); Zhiqiang Zhang (Ant Group); Jun Zhou (Ant Financial); Guannan Zhang (Ant Group); WENLIANG ZHONG (Ant Group)

MADM: A Model-agnostic Denoising Module for Graph-based Social Recommendation（上交）【MADM：一个基于图的社交推荐的模型无关去噪模块】
Wenze Ma (Shanghai Jiao Tong University)*; Yuexian Wang (Shanghai Jiao Tong University); Yanmin Zhu (Shanghai Jiao Tong University); Zhaobo Wang (Shanghai Jiao Tong University); Mengyuan Jing (Shanghai Jiao Tong University); Xuhao Zhao (Shanghai Jiao Tong University); Jiadi Yu (Shanghai Jiao Tong University); Feilong Tang (Shanghai Jiao Tong University)

Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation（蒙特利尔，快手）【协作与转换：提取项目转换为序列推荐的多查询自注意力机制】
Tianyu Zhu (University of Montreal)*; Yansong Shi (Tsinghua University); Yuan Zhang (Kuaishou Inc.); Yihong Wu (Université de Montréal); Fengran Mo (Université de Montréal); Jian-Yun Nie (Université de Montréal)

CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process（中科院）【CDRNP：通过神经过程向冷启动用户提供跨领域推荐】
Xiaodong Li (Institute of Information Engineering, Chinese Academy of Sciences); Jiawei Sheng ( Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Jiangxia Cao (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China); Tingwen Liu (Institute of Information Engineering, CAS); Wenyuan Zhang (Institute of Information Engineering, Chinese Academy of Sciences); Quangang Li (Institute of Information Engineering, CAS)
Inverse Learning with Extremely Sparse Feedback for Recommendation（卡耐基梅隆，快手）【具有极稀疏反馈的反向学习推荐】
Guanyu Lin (Carnegie Mellon University); Chen Gao (Tsinghua University); Yu Zheng (Tsinghua University); Yinfeng Li (Kuaishou Inc); Jianxin Chang (Kuaishou Inc); Yanan Niu (Kuaishou Inc); Yang Song (Kuaishou Technology); Kun Gai (AI); Zhiheng Li (Tsinghua University); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)

Contextual MAB Oriented Embedding Denoising for Sequential Recommendation（北邮）【面向上下文MAB的序列推荐嵌入去噪】
Zhichao Feng (Beijing University of Post and Telecommunications); Pengfei Wang (School of Computer Science, Beijing University of Posts and Telecommunications)*; Kaiyuan Li (Beijing University of Posts and Telecommunications); Chenliang Li (Wuhan University); Shangguang Wang (State Key Laboratory of Networking and Switching Technology)

Mixed Attention Network for Cross-domain Sequential Recommendation（卡耐基梅隆，快手）【跨域序列推荐的混合注意网络】
Guanyu Lin (Carnegie Mellon University)*; Chen Gao (Tsinghua University); Yu Zheng (Tsinghua University); Jianxin Chang (Kuaishou Inc); Yanan Niu (Kuaishou Inc); Yang Song (Kuaishou Technology); Kun Gai (AI); Zhiheng Li (Tsinghua University ); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University); Meng Wang (Institute of Artificial Intelligence, Hefei Comprehensive National Science Center)

Knowledge Graph Context-Enhanced Diversified Recommendation（伊利诺伊）【知识图谱上下文增强的多样化推荐】
Xiaolong Liu (University of Illinois at Chicago)*; Liangwei Yang (University of Illinois at Chicago); Zhiwei Liu (Salesforce); Mingdai Yang (University of Illinios at Chicago); Chen Wang (University of Illinois at Chicago); Hao Peng (Beihang University); Philip S Yu (UIC)

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights（西湖大学）【基于适配器的推荐系统迁移学习探索：实证研究与实践启示】
Junchen Fu (Westlake University)*; Fajie Yuan (Westlake University); Yu Song (Westlake University); Zheng Yuan (Westlake University); Mingyue Cheng (University of Science and Technology of China); Shenghui Cheng (Westlake University); Jiaqi Zhang (Westlake University); Jie Wang (Westlake University); Yunzhu Pan (University of Electronic Science and Technology of China)

Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation（香港城市大学，华为）【Diff-MSR：冷启动多场景推荐的扩散模型增强范式】
Yuhao Wang (City University of Hong Kong)*; Ziru Liu (City University Of HongKong ); Yichao Wang (Huawei Noah’s Ark Lab); Xiangyu Zhao (City University of Hong Kong); Bo Chen (Huawei Noah’s Ark Lab); Huifeng Guo (Huawei Noah’s Ark Lab); Ruiming Tang (Huawei Noah’s Ark Lab)

AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations（腾讯）
He Wei (Tencent Inc.)*; Meixi Liu (Machine learning platform department, Tencent TEG); Yang Zhang (Tencent Inc)

C^2DR: Robust Cross-Domain Recommendation based on Causal Disentanglement（中南）【C^2DR：基于因果解耦的鲁棒跨域推荐】
Menglin Kong (Central South University); Jia Wang (Xi’an Jiaotong-Liverpool University); Yushan Pan (Xi’an Jiaotong-Liverpool University); Haiyang Zhang (Xi’an Jiaotong-Liverpool University); Muzhou Hou (Central South Uinversity)
Unified Pretraining for Recommendation via Task Hypergraphs（伊利诺伊，Salesforce）【基于任务超图的推荐统一预训练】
Mingdai Yang (University of Illinios at Chicago); Zhiwei Liu (Salesforce); Liangwei Yang (University of Illinois at Chicago); Xiaolong Liu (University of Illinois at Chicago); Chen Wang (University of Illinois at Chicago); Hao Peng (Beihang University); Philip S Yu (UIC)

SSLRec: A Self-Supervised Learning Library for Recommendation（港大）【自监督推荐库】
Xubin Ren (the University of Hong Kong)*; Lianghao Xia (University of Hong Kong); Yuhao Yang (Wuhan University); Wei Wei (University of Hong Kong); Tianle Wang (HKU); Xuheng Cai (The University of Hong Kong); Chao Huang (University of Hong Kong)

Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation（深圳大学，腾讯）【辅助信息集成序列推荐的多序列注意用户表征学习】
Xiaolin Lin (Shenzhen University)*; Jinwei Luo (Shenzhen University); Junwei Pan (Tencent); Weike Pan (Shenzhen University); Zhong Ming (Shenzhen University); Xun Liu (Tencent); HUANG SHUDONG (tencent); Jie Jiang (Tencent Inc.)

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting（中科大，快手）【LabelCraft：通过自动标签制作实现短视频推荐】
Yimeng Bai (University of Science and Technology of China)*; Yang Zhang (University of Science and Technology of China); Jing Lu (Kuaishou Inc); Jianxin Chang (Kuaishou Inc); Xiaoxue Zang (Kuaishou Inc); Yanan Niu (Kuaishou); Yang Song (Kuaishou Technology); Fuli Feng (University of Science and Technology of China)

MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation（汉阳大学）【MONET：包含图卷积网络的模态和多媒体推荐的目标感知注意力】
Yungi Kim (Hanyang University); Taeri Kim (Hanyang University); Won-Yong Shin (Yonsei University, Korea); Sang-Wook Kim (Hanyang University, Korea)*

RecJPQ: Training Large-Catalogue Sequential Recommenders【RecJPQ：训练大型目录序列推荐】
Aleksandr V Petrov (University of Glasgow); Craig Macdonald (University of Glasgow)
On the Effectiveness of Unlearning in Session-Based Recommendation（山大）【基于会话的推荐中释放的有效性研究】
Xin Xin (Shandong University); Liu Yang (Shandong University); Ziqi Zhao (Shandong University); Pengjie Ren (Shandong University); Zhumin Chen (Shandong University); Jun Ma (Shandong University); Zhaochun Ren (Leiden University)

Proxy-based Item Representation for Attribute and Context-aware Recommendation（首尔国立大学）【基于代理的item表征】
Jinseok Seol (Seoul National University); Minseok Gang (Seoul National University); Sang-goo Lee (Seoul National University); Jaehui Park (University of Seoul)
IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation（清华，华为）【IncMSR：一种用于多场景推荐的增量学习方法】
Kexin Zhang (Tsinghua University); Yichao Wang (Huawei Noah’s Ark Lab); Xiu Li (Tsinghua University); Ruiming Tang (Huawei Noah’s Ark Lab); Rui Zhang (http://ruizhang.info)

Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation（阿里）【触发推荐中CTR预测的深度进化即时兴趣网络】
Zhibo Xiao (Alibaba Group)*; Luwei Yang (Alibaba Group); Tao Zhang (Alibaba Group); Wen Jiang (Alibaba Group); Wei Ning ( Alibaba Group); Yujiu Yang (Tsinghua University)

User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Qi Hu (Hong Kong University of Science and Technology); Yangqiu Song (Hong Kong University of Science and Technology)
Neural Kalman Filtering for Robust Temporal Recommendation（复旦，微软，亚马逊）【用于鲁棒时间推荐的神经卡尔曼滤波】
Jiafeng Xia (Fudan University); Dongsheng Li (Microsoft Research Asia); Hansu Gu (http://Amazon); Tun Lu (Fudan University); Peng Zhang (Fudan University); Li Shang (Fudan University); Ning Gu (Fudan University)

Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation（天大）【多兴趣推荐中项目嵌入增强的属性仿真】
Yaokun Liu (Tianjin University)*; Xiaowang Zhang (Tianjin University); Minghui Zou (Tianjin University); Zhiyong Feng (Tianjin University)

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure（山大）【基于系统曝光的分布鲁棒优化对序列推荐去偏】
Jiyuan Yang (Shandong University)*; Yue Ding (Shanghai Jiao Tong University); YIDAN WANG (SHANDONG UNIVERSITY); Pengjie Ren (Shandong University); Zhumin Chen (Shandong University); Fei Cai (National University of Defense Technology); Jun Ma (Shandong University); Rui Zhang (http://ruizhang.info); Zhaochun Ren (Leiden University); Xin Xin (Shandong University)

Knowledge Graph Diffusion Model for Recommendation（港大）【知识图扩散模型用于推荐】
Yangqin Jiang (University of Hong Kong)*; Yuhao Yang (Wuhan University); Lianghao Xia (University of Hong Kong); Chao Huang (University of Hong Kong)

Interact with the Explanations: Causal Debiased Explainable Recommendation System（上交，adobe）【因果去偏可解释推荐系统】
Xu Liu (Shanghai Jiao Tong University); Tong Yu (Adobe Research); Kaige Xie (Georgia Institute of Technology); Junda Wu (New York University); Shuai Li (Shanghai Jiao Tong University)*

Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation（天大）【多行为序列推荐的全局异构图和目标兴趣去噪】
Xuewei Li (Tianjin University); Hongwei Chen (College of Intelligence and Computing, Tianjin University); Jian Yu (Tianjin University); Mankun Zhao (Tianjin University); Tianyi Xu (Tianjin University); Wenbin Zhang (Information and Network Center, Tianjin University); Mei Yu (Tianjin University)
MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems【MultiFS:深度推荐系统中的自动多场景特征选择】
Dugang Liu (Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University); Chaohua Yang (Shenzhen University); Xing Tang (Tencent); Yejing Wang (City University of Hongkong); Fuyuan Lyu (McGill University); weihong luo (tencent); Xiuqiang He (Tencent); Zhong Ming (Shenzhen University); Xiangyu Zhao (City University of Hong Kong)

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction（山大，阿里）【list-wise蒸馏用于CTR预测校准】
Xiaoqiang Gui (Shandong University)*; Yueyao Cheng (Alibaba Group); Xiang-Rong Sheng (Alibaba Group); Yunfeng Zhao (Shandong University); Guoxian Yu (Shandong University); Shuguang Han (Alibaba Inc.); Yuning Jiang (Alibaba Group); Jian Xu (Alibaba Group); Bo Zheng (Alibaba Group)

ICLR 2024

转载自：https://zhuanlan.zhihu/p/669386030

1. STUDY: Socially Aware Temporally Causal Decoder Recommender Systems
   研究：社会意识时间因果解码器推荐系统

2. SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems
   SUBER：用于推荐系统的模拟人类行为的 RL 环境

3. UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
   UOEP：以用户为导向的探索政策，以增强推荐系统的长期用户体验

4. Strategic Recommendations for Improved Outcomes in Congestion Games
   改善拥堵游戏结果的战略建议

5. Categorical Features of entities in Recommendation Systems Using Graph Neural Networks
   使用图神经网络的推荐系统中实体的分类特征

6. Safe Collaborative Filtering
   安全协同过滤

7. Cross-domain Recommendation from Implicit Feedback
   来自隐性反馈的跨领域推荐

8. Disentangled Heterogeneous Collaborative Filtering

9. Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation
   注意邻域效应：在干扰下对选择偏差进行建模以进行推荐

10. Demystifying Embedding Spaces using Large Language Models

11. FIITED: Fine-grained embedding dimension optimization during training for recommender systems
    FIITED：推荐系统训练过程中的细粒度嵌入维度优化

12. From Deterministic to Probabilistic World: Balancing Enhanced Doubly Robust Learning for Debiased Recommendation
    从确定性世界到概率性世界：平衡增强型双倍鲁棒学习以实现无偏推荐

13. How Does Message Passing Improve Collaborative Filtering?
    消息传递如何改进协作过滤？

14. VibeSpace: Automatic vector embedding creation for arbitrary domains and mapping between them using large language models
    VibeSpace：使用大型语言模型为任意域自动创建向量嵌入并在它们之间进行映射

15. Unifying User Preferences and Critic Opinions: A Multi-View Cross-Domain Item-sharing Recommender System
    统一用户偏好和评论家意见：一个多视角的跨域物品共享推荐系统

16. GNN-based Reinforcement Learning Agent for Session-based Recommendation
    基于GNN的强化学习代理，用于基于会话的推荐

17. Basis Function Encoding of Numerical Features in Factorization Machines for Improved Accuracy
    因式分解机中数值特征的基函数编码以提高精度

18. MOESART: An Effective Sampling-based Router for Sparse Mixture of Experts
    MOESART：一种有效的基于采样的路由器，用于稀疏专家的混合

19. On the Embedding Collapse When Scaling up Recommendation Models
    关于扩展推荐模型时的嵌入崩溃

20. Hyperbolic Embeddings in Sequential Self-Attention for Improved Next-Item Recommendations
    顺序自注意力中的双曲线嵌入，以改进下一步建议

21. Constraining Non-Negative Matrix Factorization to Improve Signature Learning
    约束非负矩阵分解以改善特征学习

22. Farzi Data: Autoregressive Data Distillation

23. Factual and Personalized Recommendation Language Modeling with Reinforcement Learning
    基于强化学习的事实和个性化推荐语言建模

24. ConvFormer: Revisiting Token-mixers for Sequential User Modeling
    ConvFormer：重新审视用于顺序用户建模的令牌混合器

25. Talk like a Graph: Encoding Graphs for Large Language Models
    像图形一样说话：大型语言模型的编码图形

26. Weight Uncertainty in Individual Treatment Effect
    个体treatment效果的权重不确定性

27. Explaining recommendation systems through contrapositive perturbations
    通过逆向扰动解释推荐系统

28. Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
    使用有偏见的离线数据和不完美的模拟器进行强化学习的基准

29. Evidential Conservative Q-Learning for Dynamic Recommendations
    动态推荐的证据保守 Q 学习

30. UNLEARNING THE UNWANTED DATA FROM A PERSONALIZED RECOMMENDATION MODEL
    从个性化推荐模型中消除不需要的数据

31. AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
    AFDGCF：自适应特征去相关图协同过滤推荐

WWW 2024

转载自：https://zhuanlan.zhihu/p/683516906

1. Intelligent Model Update Strategy for Sequential Recommendation
   顺序推荐的智能模型更新策略
2. A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems
   用于负责任推荐系统的以数据为中心的多目标学习框架
3. User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation
   跨域推荐的协同过滤用户分布映射建模
4. Collaborative Large Language Model for Recommender Systems
   推荐系统的协作大语言模型
5. Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
   重新思考开放世界假设下的跨域顺序推荐
6. Temporal Conformity-aware Hawkes Graph Network for Recommendations
   用于建议的时间一致性感知霍克斯图网络
7. Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning
   并非所有嵌入都是平等的：通过对比学习实现稳健的跨域推荐
8. Harnessing Large Language Models for Text-Rich Sequential Recommendation
   利用大型语言模型进行丰富文本的顺序推荐
9. Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
   多模态知识蒸馏，用于快速调整的推荐
10. Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
    左下部分 AUC：一种有效且高效的推荐优化指标
11. Accurate Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
    通过基于人气的聚合和课程加热进行准确的冷启动捆绑推荐
12. Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
13. Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
    大规模推荐系统的可扩展且可证明公平的曝光控制
14. Causally Debiased Time-aware Recommendation
    因果偏差的时间感知建议
15. Uplift Modeling for Target User Attacks on Recommender Systems
    目标用户对推荐系统攻击的提升建模
16. Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
    去中心化POI推荐中的物理轨迹推理攻击与防御
17. Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation
    用于社交推荐的图对比学习与核依赖最大化
18. FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
19. FairSync：确保分布式推荐检索中的摊销组暴露
20. Top-Personalized-K Recommendation
    顶级个性化K推荐
21. Debiasing Recommendation with Personal Popularity
    利用个人知名度消除推荐偏差
22. UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
    UnifiedSSR：顺序搜索和推荐的统一框架
23. Generative News Recommendation
    生成新闻推荐
24. MMPOI: A Multi-Modal Content-Aware Framework for POI Recommendations
    MMPOI：用于 POI 推荐的多模式内容感知框架
25. Representation Learning with Large Language Models for Recommendation
    使用大型语言模型进行表示学习以进行推荐
26. Challenging Low Homophily in Social Recommendation
    挑战社交推荐中的低同质性
27. ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
    ReLLa：用于推荐中终身顺序行为理解的检索增强型大型语言模型
28. Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
    学习基于 ID 的推荐的类别树：探索可微向量量化的力量
29. Retention Depolarization in Recommender System
    推荐系统中的保留去极化
30. Linear-Time Graph Neural Networks for Scalable Recommendations
    用于可扩展推荐的线性时间图神经网络
31. Poisoning Federated Recommender Systems with Fake Users
    用虚假用户毒害联合推荐系统
32. Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation
    小语言模型可以充当推荐器吗？走向以数据为中心的冷启动建议
33. Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
    理清推荐对用户消费模式的长期影响
34. Online Billion-Scale Recommender Systems with Macro Graph Neural Networks
    具有宏图神经网络的在线十亿级推荐系统
35. Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
    用于跨域推荐的快速增强联合内容表示学习
36. Intersectional Two-sided Fairness in Recommendation
    推荐中的交叉两侧公平性
37. Link Recommendation to Augment Influence Diffusion with Provable Guarantees
    将推荐与可证明的保证增强影响力扩散联系起来
38. When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
    当联合推荐遇到冷启动问题时：分离项目属性和用户交互
39. Recommender Transformers with Behavior Pathways
    具有行为路径的推荐变压器
40. RecDCL: Dual Contrastive Learning for Recommendation
    RecDCL：双重对比学习推荐
41. Ensuring User-side Fairness in Dynamic Recommender Systems
    确保动态推荐系统中的用户端公平性
42. AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
    AgentCF：推荐系统与自主语言代理的协作学习
43. Graph Pretraining and Prompt Learning for Recommendation
    图预训练和即时学习推荐
44. Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
    镜像梯度：通过探索平坦局部最小值走向稳健的多模态推荐系统
45. Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs
    利用对比学习对 MOOC 知识概念推荐的平衡显性和隐性关系进行建模
46. Learning Counterfactual Explanations for Recommender Systems
    学习推荐系统的反事实解释
47. Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
    基于类别和流行度的视频游戏推荐：一个平衡导向的框架
48. Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning
    通过不变学习释放知识图谱的力量进行推荐
49. Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework
    通过框嵌入提高推荐准确性和多样性：通用框架
50. Leave No One Behind: Online Self-Supervised Self-distillation for Sequential Recommendation
    不让任何人掉队：在线自监督自蒸馏顺序推荐
51. Distributionally Robust Graph-based Recommendation System
    基于图的分布式鲁棒推荐系统
52. Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
    针对非随机数据缺失的建议的双重校准估计器
53. Reconciling the accuracy-diversity trade-off in recommendations
    协调推荐中的准确性与多样性权衡
54. Co-clustering for Federated Recommender System
    联合聚类联合推荐系统
55. M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
56. Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
    对比学习有必要吗？顺序推荐中数据增强与对比学习的研究
57. Can Small Language Models be Good Reasoners in Recommender Systems?
    小语言模型可以成为推荐系统中的良好推理机吗？
58. Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation
    Next-POI 建议中的负采样：观察、方法和评估
59. Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
    迈向个性化隐私：用户管理的数据贡献以实现联合推荐
60. Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
    用于隐私保护推荐的联合异构图神经网络
61. Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
    具有自适应参考数据的去中心化协作学习，用于设备上 POI 推荐
62. Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
    通过低阶训练实现高效通信和安全联合推荐系统
63. Hierarchical Graph Signal Processing for Collaborative Filtering
    用于协同过滤的分层图信号处理
64. General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
    通过对抗图 Dropout 进行基于图的协同过滤的一般去偏

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