RecSys2022推荐系统论文集锦

2022 年 7 月 10 日 机器学习与推荐算法

嘿，记得给“机器学习与推荐算法”添加星标

第16届推荐系统年会（ACM RecSys 2022）将于2022年9月18日-23日在美国西雅图举行，大会表明将以更加包容的方式通过线上与线下混合举办的形式允许更多的推荐系统研究人员与从业者参与其中。推荐系统年会侧重于探讨推荐系统研究过程中的新颖任务、关键挑战以及全新的观点，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。

历届顶级会议关于推荐系统的论文集锦可参考：

本年度的会议论文接收列表已于2022年6月29日在官方网站公布，其中包括38篇长文论文，15篇工业界演讲以及16篇海报）。值得一提的是，今年推荐系统年会共收到231篇有效投稿，最终录取了39篇推荐系统长文，录取率为17%，基本上与往年的录取率保持平衡。为了给打算投此会议的学生与老师们一个参考指标，往年的投稿数量与录取数量总结见下表。

本年度的论文接收列表官网地址：

https://recsys.acm.org/recsys22/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[16,32]、Bias问题[37]、冷启动问题[17]、对话推荐系统[5,30]、推荐系统的重复购买行为[20,23]、推荐中的隐私和安全问题[13,16,32]、推荐系统的可解释性以[26]等，与去年所关注的主题类似；

从推荐系统任务角度来看，其主要包括序列化推荐[3,7,8,9,14,18]、捆绑推荐[4,5]、对话推荐[5,30]、音乐推荐[15,35]、新闻推荐[27,29]、路线推荐[21]、职位推荐[21]等；

从推荐技术的角度来看，包括对于经典矩阵分解的改进（比如公平的联邦推荐分解[16]、基于原型的高效矩阵分解[26]）、因子分解机的改进（比如双注意力高阶因子分解机[11]），推荐系统与自然语言处理的统一建模[28]、基于对抗学习的推荐算法[2]、基于强化学习的推荐算法[25]、基于联邦学习的推荐算法[16,32]、基于自监督的推荐算法[15,30]、专门的推荐系统GPU参数服务器[1]、推荐系统的高效训练[14,31]等。

大会教程分别涉及多阶段推荐系统的排序以及训练部署、强化学习推荐、组推荐、人在回路的推荐系统、可解释推荐系统、基于心理学的推荐系统以及对话推荐系统：

Neural Re-ranking for Multi-stage Recommender Systems
by Weiwen Liu (Huawei Noah’s Ark Lab, China), Jiarui Qin (Shanghai Jiao Tong University, China), Ruiming TANG (Huawei, China), and Bo Chen (Huawei, China)
Hands-on Reinforcement learning for recommender systems – From Bandits to SlateQ to Offline RL with Ray RLlib
by Kourosh Hakhamaneshi (Anyscale, USA) and Christy Bergman (Anyscale, USA)
Offline Evaluation for Group Recommender Systems

by Francesco Barile (Maastricht University, The Netherlands), Amra Delić (University of Sarajevo, Bosnia and Herzegovina), and Ladislav Peška (Charles University, Czech Republic)
Training and Deploying Multi-Stage Recommender Systems

by Ronay Ak (NVIDIA, USA), Benedikt Schifferer (NVIDIA, Germany), Sara Rabhi (NVIDIA, Canada), and Gabriel de Souza Pereira Moreira (NVIDIA, Brazil)
Improving Recommender Systems with Human-in-the-Loop

by Dmitry Ustalov, PhD (Toloka, Switzerland), Natalia Fedorova (Toloka, Switzerland), Nikita Pavlichenko (Toloka, Switzerland)
Hands on Explainable Recommender Systems with Knowledge Graphs
by Giacomo Balloccu (University of Cagliari, Italy), Ludovico Boratto (University of Cagliari, Italy), Gianni Fenu (University of Cagliari, Italy), and Mirko Marras (University of Cagliari, Italy)
Psychology-informed Recommender Systems
by Elisabeth Lex (Graz University of Technology, Austria) and Markus Schedl (Johannes Kepler University Linz, Austria)
Conversational Recommender System Using Deep Reinforcement Learning
by Omprakash Sonie (DeepThinking.AI, India)

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 GPU-specialized Inference Parameter Server for Large-Scale DeepRecommendation Models
Yingcan Wei (NVIDIA, China), Matthias Langer (NVIDIA, China), Fan Yu (NVIDIA, China), Minseok Lee (NVIDIA, Korea, Republic of), Jie Liu (NVIDIA, China), Ji Shi (NVIDIA, China), Zehuan Wang (NVIDIA, China)
[2] Adversary or Friend? An adversarial Approach to Improving Recommender Systems
Pannaga Shivaswamy (Netflix Inc, United States) and Dario Garcia Garcia (Netflix, United States)
[3] Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation
Wei Cai (Zhejiang university, China), Weike Pan (Shenzhen University, China), Jingwen Mao (Computer Science, China), Zhechao Yu (Zhejiang University, China), congfu xu (Zhejiang University, China)
[4] BRUCE – Bundle Recommendation Using Contextualized item Embeddings
Tzoof Avny Brosh (Ben Gurion, Israel), Amit Livne (Ben-Gurion University of the Negev, Israel), Oren Sar Shalom (Facebook, Israel), Bracha Shapira (Ben-Gurion University of the Negev, Israel), Mark Last (Ben-Gurion University of the Negev, Israel)
[5] Bundle MCR: Towards Conversational Bundle Recommendation
Zhankui He (UC San Diego, United States), Handong Zhao (Adobe Research, United States), Tong Yu (Adobe Research, United States), Sungchul Kim (Adobe Research, United States), Fan Du (Adobe Research, United States), Julian McAuley (UC San Diego, United States)
[6] CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
Rui Ma (Alibaba Group, China), Ning Liu (Tsinghua University, China), Jingsong Yuan (Alibaba Group, China), Huafeng Yang (Alibaba Group, China), Jiandong Zhang (Alibaba Group, China)
[7] Context and Attribute-Aware Sequential Recommendation via Cross-Attention
Ahmed Rashed (University of Hildesheim, Germany), Shereen Elsayed (University of Hildesheim, Germany), Lars Schmidt-Thieme (University of Hildesheim, Germany)
[8] Defending Substitution-based Profile Pollution Attacks on Sequential Recommenders
Zhenrui Yue (University of Illinois Urbana-Champaign, United States), Huimin Zeng (University of Illinois Urbana-Champaign, United States), Ziyi Kou (University of Illinois Urbana-Champaign, United States), Lanyu Shang (University of Illinois Urbana-Champaign, United States), Dong Wang (University of Illinois at Urbana-Champaign, United States)
[9] Denoising Self-Attentive Sequential Recommendation
Huiyuan Chen (Visa Research, United States), Yusan Lin (Visa Research, United States), Menghai Pan (Visa Research, United States), Lan Wang (Visa Research, United States), Chin-Chia Michael Yeh (Visa Inc, United States), Xiaoting Li (Visa Research, United States), Yan Zheng (Visa Research, United States), Fei Wang (Visa Research, United States), Hao Yang (Visa Research, United States)
[10] Don’t recommend the obvious: estimate probability ratios
Roberto Pellegrini (Amazon Development Centre Scotland, United Kingdom), Wenjie Zhao (Amazon Development Centre Scotland, United Kingdom), Iain Murray (Amazon Development Centre Scotland, United Kingdom and University of Edinburgh, United Kingdom)
[11] Dual Attentional Higher Order Factorization Machines
Arindam Sarkar (Amazon, India), Dipankar Das (Amazon, India), Vivek Sembium (Amazon, India), Prakash Mandayam Comar (Amazon, India)
[12] Dynamic Global Sensitivity for Differentially Private Contextual Bandits
Huazheng Wang (Princeton University, United States), David B Zhao (University of Virginia, United States), Hongning Wang (University of Viriginia, United States)
[13] EANA: Reducing Privacy Risk on Large-scale Recommendation Models
Lin Ning (Google Research, United States), Steve Chien (Google Research, United States), Shuang Song (Google Research, United States), Mei Chen (Google, United States), Qiqi Xue (Google, United States), Devora Berlowitz (Google Research, United States)
[14] Effective and Efficient Training for Sequential Recommendation using Recency Sampling
Aleksandr Petrov (the University of Glasgow, United Kingdom) and Craig Macdonald (University of Glasgow, United Kingdom)
[15] Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
Minju Park (Seoul National University, Korea, Republic of) and Kyogu Lee (Seoul National University, Korea, Republic of)
[16] Fairness-aware Federated Matrix Factorization
Shuchang Liu (Rutgers University, United States), Yingqiang Ge (Rutgers University, United States), Shuyuan Xu (Rutgers University, United States), Yongfeng Zhang (Rutgers University, United States), Amelie Marian (Rutgers University, United States)
[17] Fast And Accurate User Cold-Start Learning Using Monte Carlo Tree Search
Dilina Chandika Rajapakse (Trinity College Dublin, Ireland) and Douglas Leith (Trinity College Dublin, Ireland)
[18] Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
Weixin Chen (Shenzhen University, China), Mingkai He (Shenzhen University, China), Yongxin Ni (National University of Singapore, Singapore), Weike Pan (Shenzhen University, China), Li Chen (Hong Kong Baptist University, Hong Kong), Zhong Ming (Shenzhen University, China)
[19] Learning Recommendations from User Actions in the Item-poor Insurance Domain
Simone Borg Bruun (University of Copenhagen, Denmark), Maria Maistro (University of Copenhagen, Denmark), Christina Lioma (University of Copenhagen, Denmark)
[20] Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
Ori Katz (Microsoft, Israel and Technion, Israel), Oren Barkan (Microsoft, Israel and The Open University, Israel), Noam Koenigstein (Microsoft, Israel and Tel-Aviv University, Israel), Nir Zabari (Microsoft, Israel and The Hebrew University, Israel)
[21] MARRS: A Framework for Multi-objective Risk-aware Route Recommendation Using Multitask-Transformer
Bhumika . (IIT Jodhpur, India) and Debasis Das (Indian Institute of Technology (IIT), India)
[21] Modeling Two-Way Selection Preference for Person-Job Fit
Chen Yang (Renmin University of China, China), Yupeng Hou (Gaoling School of Artificial Intelligence, China), Yang Song (BOSS zhipin, China), Tao Zhang (BOSS zhipin, China), Jirong Wen (Gaoling School of Artificial Intelligence, China), Wayne Xinzhao (Renmin University of China, China)
[23] Modeling User Repeat Consumption Behavior for Online Novel Recommendation
Yuncong Li (Tencent, China), Cunxiang Yin (Tencent, China), yancheng he (Tencent, China), Guoqiang Xu (Tencent, China), Jing Cai (tencent, China), leeven luo (technology zone, China), Sheng-hua Zhong (Shenzhen University, China)
[24] Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation
Yaxiong Wu (University of Glasgow, United Kingdom), Craig Macdonald (University of Glasgow, United Kingdom), Iadh Ounis (University of Glasgow, United Kingdom)
[25] Off-Policy Actor Critic for Recommender Systems
Minmin Chen (Google, United States), Can Xu (Google Inc, United States), Vince Gatto (Google, United States), Devanshu Jain (Google, United States), Aviral Kumar (Google, United States), Ed Chi (Google, United States)
[26] ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
Alessandro B. Melchiorre (Johannes Kepler University, Austria and Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria), Navid Rekabsaz (Johannes Kepler University, Austria and Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria), Christian Ganhör (Johannes Kepler University, Austria), Markus Schedl (Johannes Kepler University Linz, Austria and Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria)
[27] RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
Sanne Vrijenhoek (Universiteit van Amsterdam, Netherlands), Gabriel Bénédict (University of Amsterdam, Netherlands), Mateo Gutierrez Granada (RTL Nederland B.V., Netherlands), Daan Odijk (RTL Nederland B.V., Netherlands), Maarten de Rijke (University of Amsterdam, Netherlands)
[28] Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
Shijie Geng (Rutgers University, United States), Shuchang Liu (Rutgers University, United States), Zuohui Fu (Rutgers University, United States), Yingqiang Ge (Rutgers University, United States), Yongfeng Zhang (Rutgers University, United States)
[29] Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation
Karthik Shivaram (Tulane University, United States), Ping Liu (Illinois Institute of Technology, United States), Matthew Shapiro (Illinois Institute of Technology, United States), Mustafa Bilgic (Illinois Institute of Technology, United States), Aron Culotta (Tulane University, United States)
[30] Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales
Shuyang Li (UC San Diego, United States), Bodhisattwa Prasad Majumder (UC San Diego, United States), Julian McAuley (UC San Diego, United States)
[31] TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
Huiyuan Chen (Visa Research, United States), Xiaoting Li (Visa Research, United States), Kaixiong Zhou (Rice University, United States), Xia Hu (Rice University, United States), Chin-Chia Michael Yeh (Visa Inc, United States), Yan Zheng (Visa Research, United States), Hao Yang (Visa Research, United States)
[32] Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Kiwan Maeng (Meta, United States and Pennsylvania State University, United States), Haiyu Lu (Meta, United States), Luca Melis (Meta, United States), John Nguyen (Meta, United States), Mike Rabbat (Meta, United States), Carole-Jean Wu (Meta, United States)
[33] Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
Kohei Hirata (Osaka University, Japan), Daichi Amagata (Osaka University, Japan), Sumio Fujita (Yahoo Japan Corporation, Japan), Takahiro Hara (Osaka University, Japan)
[34] Towards Psychologically Grounded Dynamic Preference Models
Mihaela Curmei (Berkeley, United States), Andreas Haupt (Massachusetts Institute of Technology, United States), Dylan Hadfield-Menell (Massachusetts Institute of Technology, United States), Benjamin Recht (University of California – Berkeley, United States)
[35] A User-Centered Investigation of Personal Music Tours
Giovanni Gabbolini (University College Cork, Ireland) and Derek Bridge (University College Cork, Ireland)
[36] A longitudinal study – Exploring the effect of nudging on users’ genre exploration behavior and listening preference
Yu Liang (’s-Hertogenbosch, Netherlands) and Martijn C. Willemsen (Eindhoven University of Technology, Netherlands and Jheronimus Academy of Data Science, Netherlands)
[37] Countering Popularity Bias by Regularizing Score Differences
Wondo Rhee (Seoul National University, Korea, Republic of), Sung Min Cho (Seoul National University, Korea, Republic of), Bongwon Suh (Seoul National University, Korea, Republic of)
[38] Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy
Maryam Aziz (Spotify, United States), Jesse Anderton (Spotify, United States), Kevin Jamieson (University of Washington, United States), Alice Y. Wang (Spotify, United States), Hugues Bouchard (Spotify, United States), Javed Aslam (Northeastern University, United States)

欢迎干货投稿 \ 论文宣传 \ 合作交流

RecSys2022推荐系统论文集锦

推荐阅读

相关内容