Accepted Contributions

Instructions for preparing camera-ready versions of accepted papers can be found here.

List of all papers accepted for RecSys 2022 (in alphabetical order).

 

  • 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)
  • A User-Centered Investigation of Personal Music Tours
    Giovanni Gabbolini (University College Cork, Ireland) and Derek Bridge (University College Cork, Ireland)
  • 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)
  • Adversary or Friend? An adversarial Approach to Improving Recommender Systems
    Pannaga Shivaswamy (Netflix Inc, United States) and Dario Garcia Garcia (Netflix, United States)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • Dual Attentional Higher Order Factorization Machines
    Arindam Sarkar (Amazon, India), Dipankar Das (Amazon, India), Vivek Sembium (Amazon, India), Prakash Mandayam Comar (Amazon, India)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • You Say Factorization Machine, I Say Neural Network – It’s All in the Activation
    Chen Almagor (The Hebrew University of Jerusalem, Israel) and Yedid Hoshen (The Hebrew University of Jerusalem, Israel)
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