• RecSys2023推荐系统论文整理
• RecSys2022推荐系统论文集锦
  
• RecSys2021推荐系统论文集锦
  

• RecSys2020推荐系统论文集锦
  

本年度的会议论文接收列表已于近日在官方网站公布，包括58篇研究型论文、34篇应用型论文。推荐系统年会相比于其他机器学习、数据挖掘顶会来说侧重于探讨推荐系统领域更加实际的研究话题以及更加新颖的研究角度，因此我们可以期待今年的会议都会收录哪些有意思或者有想法的论文。

https://recsys.acm.org/recsys24/accepted-contributions/

通过对本次年会论文的总结发现，从所涉及的研究主题角度来看，此次大会主要聚焦在了冷启动和长尾问题[1,2,3,9,31,39,47]、推荐中的隐私安全[5]、大模型推荐[8,11,15,16,29,37,46,53]等、推荐中的偏差与去偏[9,40,41]、搜推一体[10]、简历-工作匹配[12]、异质信息建模[18]、推荐中的激励机制[20]、推荐中的公平性[21,22]、联邦推荐[23]、推荐模型对抗鲁棒性[25,26]、可解释推荐[28]、多目标优化[34]、重复消费建模[43,54]、推荐中的评价指标[49]；

从推荐系统任务角度来看，其主要包括短视频推荐[1,35]、序列推荐[3,18,42,45,46,48]、广告推荐[7]、生成式推荐[10,11]、跨域推荐[13,14,25,28]、POI推荐[16]、互惠推荐[21]、对话式推荐[22,52,56]、点击率预估[7,24,32,41,47,58]、音乐推荐[40]、外卖推荐[43]、会话推荐[54]等；

从推荐技术的角度来看，包括多模态技术[1,2,4]、预训练与微调技术[3,24,28,39,48]、多视图技术[6]、对比学习[6,11,12,27,33]、贝叶斯优化[8]、隐式矩阵分解[13,50]、蒸馏技术[15]、大模型对齐技术[16]、神经结构搜索[17]、大规模推荐系统优化[19]等、增量建模[20]、低秩适配[23,30,32]、对抗训练[25,26]、策略学习[34]、强化学习推荐[44]、扩展法则[46]。

[1] A Multi-modal Modeling Framework for Cold-start Short-video Recommendation
Gaode Chen (Kuaishou Technology), Ruina Sun (Kuaishou Technology), Yuezihan Jiang (Kuaishou Technology), Jiangxia Cao (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Han Li (Kuaishou Technology), Kun Gai (Kuaishou Technology) and Xinghua Zhang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)

[2] A Multimodal Single-branch Embedding Network for Recommendation in Cold-start and Missing Modality Scenarios
Christian Ganhör (Johannes Kepler University Linz), Marta Moscati (Johannes Kepler University Linz), Shah Nawaz (Johannes Kepler University Linz), Anna Hausberger (Johannes Kepler University Linz) and Markus Schedl (Johannes Kepler University Linz)

[3] A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics
Junting Wang (University of Illinois, Urbana-Champaign), Praneet Rathi (University of Illinois, Urbana-Champaign) and Hari Sundaram (University of Illinois, Urbana-Champaign)

[4] A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
Zixuan Yi (University of Glasgow) and Iadh Ounis (University of Glasgow)

[5] Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
Yunfan Wu (Institute of Computing Technology, Chinese Academy of Sciences), Qi Cao (Institute of Computing Technology, Chinese Academy of Sciences), Shuchang Tao (Institute of Computing Technology, Chinese Academy of Sciences), Kaike Zhang (Institute of Computing Technology, Chinese Academy of Sciences), Fei Sun (Institute of Computing Technology, Chinese Academy of Sciences) and Huawei Shen (Institute of Computing Technology, Chinese Academy of Sciences)

[6] Adaptive Fusion of Multi-View for Graph Contrastive Recommendation
Mengduo Yang (zhejiang university), Yi Yuan (zhejiang university), Jie Zhou (zhejiang university), Meng Xi (zhejiang university), Xiaohua Pan (zhejiang university), Ying Li (zhejiang university), Yangyang Wu (zhejiang university), Jinshan Zhang (zhejiang university) and Jianwei Yin (zhejiang university)

[7] AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
Yang Yang (Huawei Noah’s Ark Lab), Bo Chen (Huawei Noah’s Ark Lab), Chenxu Zhu (Huawei Noah’s Ark Lab), Menghui Zhu (Huawei Noah’s Ark Lab), Xinyi Dai (Huawei Noah Ark’s Lab), Huifeng Guo (Huawei Noah Ark’s Lab), Muyu Zhang (Huawei Noah Ark’s Lab), Zhenhua Dong (Huawei Noah Ark’s Lab) and Ruiming Tang (Huawei Noah Ark’s Lab)

[8] Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
David Austin (University of Waterloo), Anton Korikov (University of Toronto), Armin Toroghi (University of Toronto) and Scott Sanner (University of Toronto)

[9] Biased User History Synthesis for Personalized Long-Tail Item Recommendation
Keshav Balasubramanian (University of Southern California), Abdulla Alshabanah (University of Southern California), Elan Markowitz (Information Sciences Institute at the University of Southern California), Greg Ver Steeg (University of California Riverside) and Murali Annavaram (University of Southern California)

[10] Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
Gustavo Penha (Spotify), Ali Vardasbi (Spotify), Enrico Palumbo (Spotify), Marco De Nadai (Spotify) and Hugues Bouchard (Spotify)

[11] CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation
Yaoyiran Li (University of Cambridge), Xiang Zhai (Google), Moustafa Alzantot (Google), Keyi Yu (Google), Ivan Vulić (University of Cambridge), Anna Korhonen (University of Cambridge) and Mohamed Hammad (Google)

[12] ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
Xiao Yu (Columbia University), Jinzhong Zhang (Intellipro) and Zhou Yu (Columbia University)

[13] Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
Abdulaziz Samra (Skoltech), Evgeny Frolov (AIRI Labs, Skoltech), Alexey Vasilev (Sber, AI Lab), Alexander Grigorevskiy (Comparables.ai) and Anton Vakhrushev (Sber, AI Lab)

[14] Discerning Canonical User Representation for Cross-Domain Recommendation
Siqian Zhao (University at Albany – SUNY) and Sherry Sahebi (University at Albany – SUNY)

[15] Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models
Yu Cui (Zhejiang University), Feng Liu (OPPO Co Ltd), Pengbo Wang (University of Electronic Science and Technology of China), Bohao Wang (Zhejiang University), Heng Tang (Zhejiang University), Yi Wan (OPPO Co Ltd), Jun Wang (OPPO Co Ltd) and Jiawei Chen (Zhejiang University)

[16] SeCor: Aligning Semantic and Collaborative representations by Large Language Models for Next-Point-of-Interest Recommendations
Shirui Wang (Tongji University), Bohan Xie (Tongji university), Ling Ding (Tongji University), Xiaoying Gao (Tongji University), Jianting Chen (Tongji University) and Yang Xiang (Tongji University)

[17] DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
Sheng Zhang (City University of Hong Kong), Maolin Wang (City University of Hong Kong), Xiangyu Zhao (City University of Hong Kong), Ruocheng Guo (ByteDance Research), Yao Zhao (Ant Group), Chenyi Zhuang (Ant Group), Jinjie Gu (Ant Group), Zijian Zhang (Jilin University) and Hongzhi Yin (The University of Queensland)

[18] Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation
Weixin Li (Shenzhen University), Xiaolin Lin (Shenzhen University), Weike Pan (Shenzhen University) and Zhong Ming (Shenzhen University)

[19] Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark
Shijie Liu (NVIDIA), Nan Zheng (NVIDIA), Hui Kang (NVIDIA), Xavier Simmons (NVIDIA), Junjie Zhang (NVIDIA), Matthias Langer (NVIDIA), Wenjing Zhu (NVIDIA), Minseok Lee (NVIDIA) and Zehuan Wang (NVIDIA)

[20] End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
Zexu Sun (Renmin University of China), Hao Yang (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China), Dugang Liu (Shenzhen University), Yunpeng Weng (Tencent), Xing Tang (Tencent) and Xiuqiang He (FiT,Tencent)

[21] Fair Reciprocal Recommendation in Matching Markets
Yoji Tomita (CyberAgent, Inc.) and Tomohiko Yokoyama (The University of Tokyo)

[22] FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems
Qin Liu (Jinan University), Xuan Feng (Jinan University), Tianlong Gu (Jinan University) and Xiaoli Liu (Jinan University)

[23] FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations
Yuchen Ding (University of Science and Technology of China), Siqing Zhang (University of Science and Technology of China), Boyu Fan (University of Helsinki), Wei Sun (University of Science and Technology of China), Yong Liao (University of Science and Technology of China) and Pengyuan Zhou (Aarhus University)

[24] FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Hangyu Wang (Shanghai Jiao Tong University), Jianghao Lin (Shanghai Jiao Tong University), Xiangyang Li (Huawei Noah’s Ark Lab), Bo Chen (Huawei Noah’s Ark Lab), Chenxu Zhu (Huawei Noah’s Ark Lab), Ruiming Tang (Huawei Noah’s Ark Lab), Weinan Zhang (Shanghai Jiao Tong University) and Yong Yu (Shanghai Jiao Tong University)

[25] Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial Training
Jingyu Chen (Sichuan University), Lilin Zhang (Sichuan University) and Ning Yang (Sichuan University)

[26] Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Kaike Zhang (Institute of Computing Technology, CAS), Qi Cao (Institute of Computing Technology, CAS), Yunfan Wu (Institute of Computing Technology, CAS), Fei Sun (Institute of Computing Technology, CAS), Huawei Shen (Institute of Computing Technology, CAS) and Xueqi Cheng (Institute of Computing Technology, CAS)

[27] Information-Controllable Graph Contrastive Learning for Recommendation
Zirui Guo (Beijing University of Posts and Telecommunications), Yanhua Yu (Beijing University of Posts and Telecommunications), Yuling Wang (Beijing University of Posts and Telecommunications), Kangkang Lu (Beijing University Of Posts and Telecommunications), Zixuan Yang (Beijing University Of Posts and Telecommunications), Liang Pang (Institute of Computing Technology, Chinese Academy of Sciences) and Tat-Seng Chua (National University of Singapore)

[28] Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations
Alessandro Petruzzelli (University of Bari Aldo Moro), Cataldo Musto (University of Bari Aldo Moro), Lucrezia Laraspata (University of Bari Aldo Moro), Ivan Rinaldi (University of Bari Aldo Moro), Marco de Gemmis (University of Bari Aldo Moro), Pasquale Lops (University of Bari Aldo Moro) and Giovanni Semeraro (University of Bari Aldo Moro)

[29] LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
Zhizhong Wan (Meituan), Bin Yin (Meituan), Junjie Xie (Meituan), Fei Jiang (Meituan), Xiang Li (Meituan) and Wei Lin (Meituan)

[30] Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation
Alex Shtoff (Yahoo Research), Michael Viderman (Yahoo Research), Naama Haramaty-Krasne (No affiliation), Oren Somekh (Yahoo Research), Ariel Raviv (No affiliation) and Tularam Ban (Yahoo Research)

[31] MARec: Metadata Alignment for cold-start Recommendation
Julien Monteil (Amazon Machine Learning), Volodymyr Vaskovych (Amazon Machine Learning), Wentao Lu (Amazon Machine Learning), Anirban Majumder (Amazon Machine Learning) and Anton van den Hengel (University of Adelaide)

[32] MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
Zhiming Yang (Northwestern Polytechnical University), Haining Gao (Alibaba Group), Dehong Gao (Northwestern Polytechnical University), Luwei Yang (Alibaba Group), Libin Yang (Northwestern Polytechnical University), Xiaoyan Cai (Northwestern Polytechnical University), Wei Ning (Alibaba Group) and Guannan Zhang (Alibaba Group)

[33] MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations
Yuezihan Jiang (Kuaishou Technology), Changyu Li (Kuaishou Technology), Gaode Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Peiyi Li (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Peng Jiang (Kuaishou Inc.), Fei Sun (Chinese Academy of Sciences, Beijing, China) and Wentao Zhang (Peking University)

[34] Multi-Objective Recommendation via Multivariate Policy Learning
Olivier Jeunen (ShareChat), Jatin Mandav (ShareChat), Ivan Potapov (ShareChat), Nakul Agarwal (ShareChat), Sourabh Vaid (ShareChat), Wenzhe Shi (ShareChat) and Aleksei Ustimenko (ShareChat)

[35] Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
Lulu Dong (East China Normal University), Guoxiu He (East China Normal University) and Aixin Sun (Nanyang Technological University)

[36] Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye, Yining Hua, Jae Boum Kim, Fangzhao Wu and Sunghun Kim

[37] Only a Controllable Reasoning Pool is Enough! Effortlessly Integrating Large Language Models’ Insights into Industrial Recommenders
Changxin Tian (Ant Group), Binbin Hu (Ant Group), Chunjing Gan (Ant Group), Haoyu Chen (Ant Group), Zhuo Zhang (Ant Group), Li Yu (Ant Group), Ziqi Liu (Ant Group), Zhiqiang Zhang (Ant Financial Services Group), Jun Zhou (Ant Financial) and Jiawei Chen (Zhejiang University)

[38] Optimal Baseline Corrections for Off-Policy Contextual Bandits
Shashank Gupta (University of Amsterdam, The Netherlands), Olivier Jeunen (ShareChat), Harrie Oosterhuis (Radboud University) and Maarten de Rijke (University of Amsterdam)

[39] Prompt Tuning for Item Cold-start Recommendation
Yuezihan Jiang (Kuaishou Technology), Gaode Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Wenhan Zhang (Peking University), Jingchi Wang (Peking University), Yinjie Jiang (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Peng Jiang (Kuaishou Technology) and Kaigui Bian (Peking University)

[40] Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders
Robin Ungruh (Delft University of Technology), Karlijn Dinnissen (Utrecht University), Anja Volk (Utrecht University), Maria Soledad Pera (Delft University of Technology) and Hanna Hauptmann (Utrecht University)

[41] Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space
Yu Liu (School of Artificial Intelligence, Nanjing University), Qinglin Jia (Huawei Noah’s Ark Lab), Shuting Shi (Huawei TECHNOLOGIES Co., Ltd), Chuhan Wu (Huawei Noah’s Ark Lab), Zhaocheng Du (Huawei Noah’s Ark Lab), Zheng Xie (Nanjing University), Ruiming Tang (Huawei Noah’s Ark Lab), Muyu Zhang (Huawei TECHNOLOGIES Co., Ltd) and Ming Li (School of Artificial Intelligence, Nanjing University)

[42] Repeated Padding for Sequential Recommendation
Yizhou Dang (Northeastern University), Yuting Liu (Northeastern University), Enneng Yang (Northeastern University), Guibing Guo (Northeastern University), Linying Jiang (Northeastern University), Xingwei Wang (Northeastern University) and Jianzhe Zhao (Northeastern University)

[43] Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery
Jiayu Li (Tsinghua University), Aixin Sun (Nanyang Technological University), Weizhi Ma (Tsinghua University), Peijie Sun (Hefei University of Technology, School of Computer and Information) and Min Zhang (Tsinghua University)

[44] RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
Shuo Su (Kuaishou Technology), Xiaoshuang Chen (Kuaishou Technology), Yao Wang (Kuaishou Technology), Yulin Wu (Kuaishou Technology), Ziqiang Zhang (Tsinghua University), Kaiqiao Zhan (Kuaishou Technology), Ben Wang (Kuaishou Technology) and Kun Gai (No affiliation)

[45] Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
Gleb Mezentsev (Skoltech), Danil Gusak (Skoltech, HSE), Ivan Oseledets (AIRI, Skoltech) and Evgeny Frolov (AIRI, HSE, Skoltech)

[46] Scaling Law of Large Sequential Recommendation Models
Gaowei Zhang (Renmin University of China), Yupeng Hou (University of California San Diego), Hongyu Lu (WeChat, Tencent), Yu Chen (WeChat, Tencent), Wayne Xin Zhao (Renmin University of China) and Ji-Rong Wen (Renmin University of China)

[47] Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
Wenhao Li (Huazhong University of Science and Technology), Jie Zhou (Beihang University), Chuan Luo (Beihang University), Chao Tang (Meituan), Kun Zhang (Meituan) and Shixiong Zhao (The University of Hong Kong)

[48] The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
Zekai Qu (China University of Geosciences Beijing), Ruobing Xie (Tencent Inc.), Chaojun Xiao (Tsinghua University), Zhanhui Kang (Tencent Inc.) and Xingwu Sun (Tencent Inc.)

[49] The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
Omar Besbes (Columbia University), Yash Kanoria (Columbia University) and Akshit Kumar (Columbia University)

[50] The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic View
Joey De Pauw (University of Antwerp) and Bart Goethals (University of Antwerp)

[51] Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
Xing Tang (Tencent), Yang Qiao (FIT, Tencent), Fuyuan Lyu (McGill University), Dugang Liu (Shenzhen University) and Xiuqiang He (FiT,Tencent)

[52] Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang (Shandong University), Ruobing Xie (Tencent), Yougang Lyu (Shandong University), Xin Xin (Shandong University), Pengjie Ren (Shandong University), Mingfei Liang (Tencent), Bo Zhang (Tencent), Zhanhui Kang (Tencent), Maarten de Rijke (University of Amsterdam) and Zhaochun Ren (Leiden University)

[53] Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi (Shanghai Jiao Tong University), Weiwen Liu (Huawei Noah’s Ark Lab), Jianghao Lin (Shanghai Jiao Tong University), Xiaoling Cai (Consumer Business Group, Huawei), Hong Zhu (Consumer Business Group, Huawei), Jieming Zhu (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) and Yong Yu (Shanghai Jiao Tong University)

[54] Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Viet-Anh Tran (Deezer Research), Guillaume Salha-Galvan (Deezer Research), Bruno Sguerra (Deezer Research) and Romain Hennequin (Deezer Research)

[55] Unified Denoising Training for Recommendation
Haoyan Chua (Nanyang Technological University), Yingpeng Du (Peking University), Zhu Sun (Agency for Science, Technology and Research (A*STAR)), Ziyan Wang (Nanyang Technological University), Jie Zhang (Nanyang Technological University) and Yew-Soon Ong (Nanyang Technological University)

[56] Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender Systems
Ting Yang (Hong Kong Baptist University) and Li Chen (Hong Kong Baptist University)

[57] Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
Yuhan Zhao (Harbin Engineering University), Rui Chen (Harbin Engineering University), Qilong Han (Harbin Engineering University), Hongtao Song (Harbin Engineering University) and Li Chen (Hong Kong Baptist University)

[58] Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate Prediction
Jiahui Huang (University of Science and Technology of China), Lan Zhang (University of Science and Technology of China), Junhao Wang (University of Science and Technology of China), Shanyang Jiang (University of Science and Technology of China), Dongbo Huang (Tencent), Cheng Ding (Tencent) and Lan Xu (Tencent)

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