RecSys2023推荐系统论文整理(长文)
第17届推荐系统年会（ACM RecSys）将在2023年9月18日到22日于新加坡举办。值得注意的是，本届年会是疫情发生以来的首次线下参会，大家可以在线下共享推荐系统领域的最新研究进展（面基）。并且这也是RecSys入选CCF推荐列表B类会议的首次会议，相信未来会有更多研究者瞄准这一会议进行投稿。
历届推荐系统年会的论文集锦可参考：
本年度的长文会议论文接收列表已于2023年6月26日在官方网站公布，包括47篇长文论文（去年长文录用为38篇）。截至目前，短文会议论文的结果目前还未公布，大家可以再期待一下。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。
本年度的论文接收列表官网地址：
https://recsys.acm.org/recsys23/accepted-contributions/
通过对本次年会论文以及教程的总结发现，从所涉及的研究主题角度来看，此次大会主要聚焦在了推荐系统中的公平性[41,45,47]、冷启动和长尾问题[4,19,32]、负采样[5,17,24]、多行为多任务[7,32,36,38]、推荐模型的重复利用[10]、过滤气泡[25]、多模态推荐[21]、可信推荐[44]、推荐系统评测[46]等；
从推荐系统任务角度来看，其主要包括序列推荐[2,8,13,15,16,24,30,35]、CTR预估[6,11,23]、对话推荐[4,21]、新闻推荐[22,25]、跨域推荐[2,14,15,17]、互惠推荐[18,35]、视频推荐[45]等；
从推荐技术的角度来看，包括对经典协同过滤方法的改进[3,5]、对比学习[2,8，15,16,31]、预训练技术[32]、图嵌入技术[34]、Transformer[39]、基于强化学习的推荐[20,33]等。
大会教程（Tutorials）分别涉及最近超火的基于大语言模型的推荐系统[1]、推荐系统离线评估的挑战与实际的评估方法[2,5]、基于深度学习的用户行为建模[3]、可信推荐系统[4]以及顾客终身价值预测[6]：
- Tutorial on Large Language Models for Recommendation. by Wenyue Hua (Rutgers University), Lei Li (Hong Kong Baptist University), Shuyuan Xu (Rutgers University), Li Chen (Hong Kong Baptist University), Yongfeng Zhang (Rutgers University)
- On Challenges of Evaluating Recommender Systems in Offline Setting. by Aixin Sun (Nanyang Technological University, Singapore)
- User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. by Weiwen Liu (Huawei Noah’s Ark Lab, China), Wei Guo (Huawei Noah’s Ark Lab, Singapore), Yong Liu (Huawei Noah’s Ark Lab, Singapore), Ruiming Tang (Huawei Noah’s Ark Lab, China), Hao Wang (University of Science and Technology of China, China)
- Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives. by Markus Schedl (Johannes Kepler University Linz and Linz Institute of Technology, Austria), Vito Walter Anelli (Politecnico di Bari, Italy), Elisabeth Lex (Graz University of Technology, Austria)
- Recommenders in the Wild / Practical Evaluation Methods. by Kim Falk (Binary Vikings), Morten Arngren (WundermanThompson)
- Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System Objectives. by Chuhan Wu (Noah’s Ark Lab, Huawei), Qinglin Jia (Noah’s Ark Lab, Huawei), Zhenhua Dong (Noah’s Ark Lab, Huawei), Ruiming Tang (Noah’s Ark Lab, Huawei)
最后，按照惯例为大家收集整理了该年会的论文列表，等论文正式发布后大家可以对自己感兴趣或者自己研究方向的论文进行更深入的阅读。
[1] A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions Norman Knyazev and Harrie Oosterhuis
[2] A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation Zitao Xu, Weike Pan and Zhong Ming
[3] Adversarial Collaborative Filtering for Free Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das and Hao Yang
[4] Alleviating the Long-Tail Problem in Conversational Recommender Systems Zhipeng Zhao, Kun Zhou, Xiaolei Wang, Wayne Xin Zhao, Fan Pan, Zhao Cao and Ji-Rong Wen
[5] Augmented Negative Sampling for Collaborative Filtering Yuhan Zhao, Rui Chen, Riwei Lai, Qilong Han, Hongtao Song and Li Chen
[6] AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction Yujun Li, Xing Tang, Bo Chen, Yimin Huang, Ruiming Tang and Zhenguo Li
[7] BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation Qianzhen Rao, Yang Liu, Weike Pan and Zhong Ming
[8] Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation Yichi Zhang, Guisheng Yin and Yuxin Dong
[9] Correcting for Interference in Experiments: A Case Study at Douyin Vivek Farias, Hao Li, Tianyi Peng, Xinyuyang Ren, Huawei Zhang and Andrew Zheng
[10] Data-free Knowledge Distillation for Reusing Recommendation Models Cheng Wang, Jiacheng Sun, Zhenhua Dong, Jieming Zhu, Zhenguo Li, Ruixuan Li and Rui Zhang
[11] Deep Situation-Aware Interaction Network for Click-Through Rate Prediction Yimin Lv, Shuli Wang, Beihong Jin, Yisong Yu, Yapeng Zhang, Jian Dong, Yongkang Wang, Xingxing Wang and Dong Wang
[12] Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction Youchen Sun, Zhu Sun, Xiao Sha, Jie Zhang and Yew Soon Ong
[13] Distribution-based Learnable Filters with Side Information for Sequential Recommendation Haibo Liu, Zhixiang Deng, Liang Wang, Jinjia Peng and Shi Feng
[14] Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation Jiajie Zhu, Yan Wang, Feng Zhu and Zhu Sun
[15] DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender Xiaoxin Ye, Yun Li and Lina Yao
[16] Equivariant Contrastive Learning for Sequential Recommendation Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang and Sunghun Kim
[17] Exploring False Hard Negative Sample in Cross-Domain Recommendation Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin and Jie Zhou
[18] Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets Yoji Tomita, Riku Togashi, Yuriko Hashizume and Naoto Ohsaka
[19] Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation Zhen Gong, Xin Wu, Lei Chen, Zhenzhe Zheng, Shengjie Wang, Anran Xu, Chong Wang and Fan Wu
[20] Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach Zhi Zheng, Ying Sun, Xin Song, Hengshu Zhu and Hui Xiong
[21] Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback Yaxiong Wu, Craig Macdonald and Iadh Ounis
[22] Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations Boming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong and Irene Li
[23] Gradient Matching for Categorical Data Distillation in CTR Prediction Cheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li and Rui Zhang
[24] gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling Aleksandr V. Petrov and Craig Macdonald
[25] How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News Lien Michiels, Jorre Vannieuwenhuyze, Jens Leysen, Robin Verachtert, Annelien Smets and Bart Goethals
[26] Incentivizing Exploration in Linear Bandits under Information Gap Huazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu and Hongning Wang
[27] InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models Kabir Nagrecha, Lingyi Liu, Pablo Delgado and Prasanna Padmanabhan
[28] KGTORe: Tailored Recommendations through Knowledge-aware GNN Models Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia and Eugenio Di Sciascio
[29] Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang and Jin Dong
[30] Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping Ming Li, Mozhdeh Ariannezhad, Andrew Yates and Maarten de Rijke
[31] Multi-Relational Contrastive Learning for Recommendation Wei Wei, Lianghao Xia and Chao Huang
[32] Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You and Philip Yu
[33] Online Matching: A Real-time Bandit System for Large-scale Recommendations Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen and Ed Chi
[34] Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang and Zhong Ming
[35] Reciprocal Sequential Recommendation Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang Song and Hengshu Zhu
[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] SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation Andreas Peintner, Amir Reza Mohammadi and Eva Zangerle
[38] STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation Wanda Li, Wenhao Zheng, Xuanji Xiao and Suhang Wang
[39] STRec: Sparse Transformer for Sequential Recommendations Chengxi Li, Xiangyu Zhao, Yejing Wang, Qidong Liu, Wanyu Wang, Yiqi Wang, Lixin Zou, Wenqi Fan and Qing Li
[40] Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning Xuewen Tao, Mingming Ha, Qiongxu Ma, Hongwei Cheng, Wenfang Lin and Xiaobo Guo
[41] Towards Robust Fairness-aware Recommendation Hao Yang, Zhining Liu, Zeyu Zhang, Chenyi Zhuang and Xu Chen
[42] Towards the Understanding and Modeling of Passive-Negative Feedback in Sequential Short-video Recommendation Yunzhu Pan, Chen Gao, Yang Song, Kun Gai, Depeng Jin and Yong Li
[43] Trending Now: Modeling Trend Recommendations Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras and Hao Wang
[44] Two-sided Calibration for Quality-aware Responsible Recommendation Chenyang Wang, Yankai Liu, Yuanqing Yu, Weizhi Ma, Min Zhang, Yiqun Liu, Haitao Zeng, Junlan Feng and Chao Deng
[45] Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong and Ji-Rong Wen
[46] What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems’ Performance using Item Response Theory Yang Liu, Alan Medlar and Dorota Glowacka
[47] When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation Jiakai Tang, Shiqi Shen, Zhipeng Wang, Zhi Gong, Jingsen Zhang and Xu Chen