RecSys 2022 推荐系统论文来了 - 文章 - 开发者社区

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来源：RecSys 2022官网

整理：秋枫学习笔记

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RecSys 2022将在西雅图召开，接收的论文已经出了，这里进行了简单的罗列和整理，主要包含联邦学习，知识图谱，注意力机制，Transformer，强化学习等技术；涉及序列推荐，公平性，多样性，冷启动，捆绑推荐，大规模推荐系统等多个领域；多家公司大厂在列，阿里，谷歌，英伟达，Visa，亚马逊等，希望对大家有帮助

以下为论文地址：

https://recsys.acm.org/recsys22/accepted-contributions/#content-tab-1-0

A GPU-specialized Inference Parameter Server for Large-Scale DeepRecommendation Models【用于大规模深度推荐模型的 GPU 专用推理参数服务器】 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【以用户为中心的个人音乐之旅调查】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【探索轻推对用户类型探索行为和聆听偏好的影响】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【对手还是朋友？改进推荐系统的对抗方法】Pannaga Shivaswamy (Netflix Inc, United States) and Dario Garcia Garcia (Netflix, United States)
Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation【在序列推荐中学习更好的商品embedding】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【BRUCE – 使用上下文商品embedding的捆绑推荐】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【MCR Bundle：迈向对话式捆绑推荐】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【CAEN：在不断增长的电子商务环境中，用于商品属性变化感知推荐的分层注意力进化网络】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【通过交叉注意力的上下文和属性感知序列推荐】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【通过规范分数差异来应对流行度偏差】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【攻防】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【去噪自注意力序列推荐】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【不推荐明显的：估计概率比】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【双注意力高阶因子分解机】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【差分私有CB的动态全局灵敏度】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【EANA：降低大规模推荐模型的隐私风险】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【使用新近抽样对序列推荐进行有效且高效的培训】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【通过对比学习利用基于内容的音乐推荐中的负面偏好】Minju Park (Seoul National University, Korea, Republic of) and Kyogu Lee (Seoul National University, Korea, Republic of)
Fairness-aware Federated Matrix Factorization【公平感知的联邦矩阵分解】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【使用蒙特卡洛树搜索进行快速准确的用户冷启动学习】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【通过学习行为转换和用户意图进行异构序列推荐的全局和个性化图】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【探索】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【从商品贫乏保险领域的用户行为中学习】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【下一次购物篮回购推荐的超卷积模型】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【MARRS：使用 Multitask-Transformer 的多目标风险感知路径推荐框架】Bhumika . (IIT Jodhpur, India) and Debasis Das (Indian Institute of Technology (IIT), India)
Modeling Two-Way Selection Preference for Person-Job Fit【为个人-工作匹配建模双向选择偏好】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【在线小说推荐的用户重复消费行为建模】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【交互式时尚推荐的多模态对话状态跟踪】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【推荐系统中的异策略 Actor Critic】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【ProtoMF：用于有效且可解释推荐的基于原型的矩阵分解】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【衡量新闻推荐中规范多样性的等级感知差异指标】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)【统一的预训练、个性化提示和预测范式】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【减少基于内容的新闻推荐中的跨主题政治同质化】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【自监督的会话推荐】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【高效地解决多样性感知的最大内部产品搜索】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【TinyKG：知识图谱神经推荐系统】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【迈向公平的联合推荐学习：表征系统和数据异质性的相互依赖】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【迈向以心理为基础的动态偏好模型】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)