Papers related to the Recommender System from KDD 2021 (including the links for Paper PDF and Github Code)
- R: Research Track Papers
- A: Applied Data Science Track Papers
[R1] Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data
Authors: Hengtong Zhang (University at Buffalo)*; Changxin Tian (Renmin University of China); Yaliang Li (Alibaba Group); Lu Su (SUNY Buffalo); Jing Gao (University at Buffalo); Nan Yang (The school of Information, Renmin University of China); Wayne Xin Zhao (Renmin University of China)
[R2] Deconfounded Recommendation for Alleviating Bias Amplification
Authors: Wenjie Wang (National University of Singapore)*; Fuli Feng (National University of Singapore); Xiangnan He (University of Science and Technology of China); Xiang Wang (National University of Singapore); Tat-Seng Chua (National university of Singapore)
[R3] Efficient Collaborative Filtering via Data Augmentation and Step-size Optimization
Authors: Xuejun Liao (SAS Institute Inc. )*; Patrick Koch (SAS Institute Inc.); Shunping Huang (SAS Institute Inc.); Yan Xu (SAS Institute Inc.)
[R4] Efficient Data-specific Model Search for Collaborative Filtering
Authors: Chen Gao (Tsinghua University)*; Quanming Yao (4Paradigm); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)
[R5] Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
Authors: Yinan Zhang (School of Computer Science and Engineering, Nanyang Technological University)*; Boyang Li (Nanyang Technological University); Yong Liu (Nanyang Technological University); Hao Wang (Alibaba Group); Chunyan Miao (NTU)
[R6] Learning Elastic Embeddings for Customizing On-Device Recommenders
Authors: Tong Chen (The University of Queensland)*; Hongzhi Yin (The University of Queensland); Yujia Zheng (University of Electronic Science and Technology of China); Zi Huang (University of Queensland); Yang Wang (Hefei University of Technology); Meng Wang (Hefei University of Technology)
[R7] Learning to Embed Categorical Features without Embedding Tables for Recommendation
Authors: Wang-Cheng Kang (Google)*; Zhiyuan Cheng (Google); Tiansheng Yao (Google); Xinyang Yi (Google); Ting Chen (Google); Lichan Hong (Google); Ed H. Chi (Google)
[R8] Learning to Recommend Visualizations from Data
Authors: Xin Qian (University of Maryland, College Park)*; Ryan A. Rossi (Adobe Research); Fan Du (Adobe Research); Sungchul Kim (Adobe); Eunyee Koh (Adobe); Sana Malik (Adobe); Tak Yeon Lee (Adobe Research); Joel Chan (University of Maryland)
[R9] MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
Authors: Tinglin Huang (Zhejiang University)*; Yuxiao Dong (Facebook AI); Ming Ding (Tsinghua University); Zhen Yang (Tsinghua University); Wenzheng Feng (Tsinghua University); Xinyu Wang (Zhejiang University); Jie Tang (Tsinghua University)
[R10] Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Authors: Tianxin Wei (University of Science and Technology of China)*; Fuli Feng (National University of Singapore); Jiawei Chen (University of Science and Technology of China); Ziwei Wu (University of Science and Technology of China); Jinfeng Yi (JD AI Research); Xiangnan He (University of Science and Technology of China)
[R11] Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation
Authors: Jiawei Zheng (South China University of Technology); Qianli Ma (South China University of Technology)*; Hao Gu (Tencent Technology (SZ) Co., Ltd.); Zhenjing Zheng (South China University of Technology)
[R12] Popularity Bias in Dynamic Recommendation
Authors: Ziwei Zhu (Texas A&M University)*; Yun He (Texas A&M University); Xing Zhao (Texas A&M University); James Caverlee (Texas A&M University)
[R13] Preference Amplification in Recommender Systems
Authors: Dimitris Kalimeris (Harvard); Smriti Bhagat (Facebook)*; Shankar Kalyanaraman (Facebook); Udi Weinsberg (Facebook)
[R14] PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
Authors: Yao Zhou (University of Illinois at Urbana-Champaign)*; Jianpeng Xu (Walmart Labs); Jun Wu (University of Illinois at Urbana–Champaign); Zeinab Taghavi Nasrabadi (Walmart Labs); Evren Korpeoglu (Walmart Labs); Kannan Achan (Walmart Labs); Jingrui He (University of Illinois at Urbana-Champaign)
[R15] Socially-Aware Self-Supervised Tri-Training for Recommendation
Authors: Junliang Yu (University of Queesland); Hongzhi Yin (The University of Queensland)*; Min Gao (Chongqing University); Xin Xia (The University of Queensland); Xiangliang Zhang (" King Abdullah University of Science and Technology, Saudi Arabia"); Quoc Viet Hung Nguyen (Griffith University)
[R16] Table2Charts: Recommending Charts by Learning Shared Table Representations
Authors: Mengyu Zhou (Microsoft Research)*; Qingtao Li (Peking University); Xinyi He (Xi’an Jiaotong University); Yuejiang Li (Tsinghua University); Yibo Liu (New York University); Wei Ji (Microsoft); Shi Han (Microsoft Research); Yining Chen (Microsoft); Daxin Jiang (Microsoft, Beijing, China); Dongmei Zhang (Microsoft Research Asia)
[R17] Topology Distillation for Recommender System
Authors: SeongKu Kang (POSTECH)*; Junyoung Hwang (POSTECH); Wonbin Kweon (POSTECH); Hwanjo Yu (POSTECH)
[R18] Towards a Better Understanding of Linear Models for Recommendation
Authors: Ruoming Jin (Kent State University)*; Dong Li (Kent State University); Jing Gao (iLambda); Zhi Liu (iLambda); Li Chen (iLambda); Yang Zhou (Auburn University)
[R19] Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems
Authors: Chenwang Wu (University of Science and Technology of China)*; Defu Lian (University of Science and Technology of China); Yong Ge (The University of Arizona); Zhihao Zhu (University of Science and Technology of China); Enhong Chen (University of Science and Technology of China)
[R20] Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Authors: Sixiao Zhang (University of Technology Sydney); Hongxu Chen (University of Technology Sydney)*; Xiao Ming (ShanDong University); Lizhen Cui (ShanDong University); Hongzhi Yin (The University of Queensland); Guandong Xu (University of Technology Sydney, Australia)
[A1] A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
Authors: Léa Briand (Deezer); Guillaume Salha-Galvan (Deezer / École polytechnique)*; Walid Bendada (Deezer); Mathieu Morlon (Deezer); Viet-Anh Tran (Deezer)
[A2] Adversarial Feature Translation for Multi-domain Recommendation
Authors: Xiaobo Hao (WeChat Search Application Department, Tencent); Yudan Liu (WeChat Search Application Department, Tencent); Ruobing Xie (WeChat Search Application Department, Tencent)*; Kaikai Ge (WeChat Search Application Department, Tencent); Linyao Tang (WeChat Search Application Department, Tencent); Xu Zhang (WeChat Search Application Department, Tencent); Leyu Lin (WeChat Search Application Department, Tencent)
[A3] Architecture and Operation Adaptive Network for Online Recommendations
Authors: Lang Lang (Didi Chuxing); zhenlong zhu (Didi Chuxing); Xuanye Liu (Didi Chuxing); Jianxin Zhao (Didi Chuxing); Jixing Xu (Didi Chuxing)*; Minghui Shan (Didi Chuxing)
[A4] Automated Loss Function Search in Recommendations
Authors: Xiangyu Zhao (Michigan State University)*; Haochen Liu (Michigan State University); Wenqi FAN (The Hong Kong Polytechnic University); Hui Liu (Michigan State University); Jiliang Tang (Michigan State University); Chong Wang (ByteDance)
[A5] Bootstrapping Recommendations at Chrome Web Store
Authors: Zhen Qin (Google)*; Honglei Zhuang (Google Research); Rolf Jagerman (Google Research); Xinyu Qian (Google Inc.); Po Hu (Google Inc.); Dan Chary Chen (Google Inc.); Xuanhui Wang (Google); Michael Bendersky (Google); Marc Najork (Google)
[A6] Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Authors: Chang Zhou (Alibaba Group); Jianxin Ma (Alibaba Group)*; Jianwei Zhang (Alibaba Group); Jingren Zhou (Alibaba Group); Hongxia Yang (Alibaba Group)
[A7] Curriculum Meta-Learning for Next POI Recommendation
Authors: Yudong Chen (Tsinghua University)*; Xin Wang (Tsinghua University); Miao Fan (Baidu); Jizhou Huang (Baidu); Shengwen Yang (Baidu); Wenwu Zhu (Tsinghua University)
[A8] Debiasing Learning based Cross-domain Recommendation
Authors: Siqing Li (Renmin University of China)*; Liuyi Yao (Alibaba Group); Shanlei Mu (Renmin University of China); Wayne Xin Zhao (Renmin University of China); Yaliang Li (Alibaba Group); Tonglei Guo (Alibaba Group); Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group"); Ji-Rong Wen (Renmin University of China)
[A9] Device-Cloud Collaborative Learning for Recommendation
Authors: Jiangchao Yao (Damo Academy, Alibaba Group)*; Feng Wang (Alibaba Group); Kunyang Jia (DAMO Academy, Alibaba Group); Bo Han (HKBU / RIKEN); Jingren Zhou (Alibaba Group); Hongxia Yang (Alibaba Group)
[A10] FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters
Authors: Wenqi Jiang (ETH Zurich)*; Zhenhao He (ETH Zurich); Shuai Zhang (ETH Zurich); Kai Zeng (Alibaba Group); Liang Feng (Alibaba Group); Jiansong Zhang (Alibaba Group); Tongxuan Liu (Alibaba Group); Yong Li (Alibaba Group); Jingren Zhou (Alibaba Group); Ce Zhang (ETH); Gustavo Alonso (ETHZ)
[A11] Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters
Authors: Yuzhen Huang (Facebook Inc.)*; Xiaohan Wei (Facebook); Xing Wang (Facebook Inc.); Jiyan Yang (Facebook Inc.); Bor-Yiing Su (Facebook); Shivam Bharuka (Facebook); Dhruv Choudhary (Facebook Inc.); Zewei Jiang (Facebook); Hai Zheng (Facebook); Jack Langman (Facebook)
[A12] Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Authors: Sanshi Yu (University of Science and Technology of China); Zhuoxuan Jiang (JD AI Research)*; Dong-Dong Chen (JD AI Research); Shanshan Feng (Harbin Institute of Technology, Shenzhen); Dongsheng Li (Microsoft Research Asia); Qi Liu (" University of Science and Technology of China, China"); Jinfeng Yi (JD AI Research)
[A13] Recommending the Most Effective Interventions to Improve Employment for Job Seekers with Disability
Authors: Ha Xuan TRAN (University of South Australia)*; Thuc Duy Le (University of South Australia); Jiuyong Li (University of South Australia); Lin Liu (University of South Australia); Jixue Liu (University of South Australia); Yanchang Zhao (CSIRO); Tony Waters (Maxima Training Group (Aust) Ltd.)
[A14] SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations
Authors: Chenyi Lei (University of Science and Technology of China, Alibaba Group)*; Yong Liu (Nanyang Technological University); lingzi zhang (Nanyang Technological University); Guoxin Wang (Alibaba Group); Haihong Tang (Alibaba Group); Houqiang Li (University of Science and Technology of China); Chunyan Miao (NTU)
[A15] Sliding Spectrum Decomposition for Diversified Recommendation
Authors: Yanhua Huang (Xiaohongshu)*; Weikun Wang (Xiaohongshu); Lei Zhang (Xiaohongshu); Ruiwen Xu (Xiaohongshu)
[A16] Towards the D-Optimal Online Experiment Design for Recommender Selection
Authors: Da Xu (Walmart Labs)*; Chuanwei Ruan (Walmart Labs); Evren Korpeoglu (Walmart Labs); Sushant Kumar (Walmart Labs); Kannan Achan (Walmart Labs)
[A17] Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
Authors: Vipul Gupta (UC Berkeley)*; Dhruv Choudhary (Facebook Inc.); Peter Tang (Facebook Inc.); Xiaohan Wei (Facebook); Yuzhen Huang (Facebook Inc.); Xing Wang (Facebook Inc.); Arun Kejariwal (Facebook Inc.); Ramchandran Kannan (Department of Electrical Engineering and Computer Science University of California, Berkeley); Michael Mahoney ("University of California, Berkeley")
[A18] We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Authors: Liyi Guo (Shanghai Jiao Tong University)*; Junqi Jin (Alibaba Group); Haoqi Zhang (Shanghai Jiao Tong University); ZHENZHE ZHENG (Shanghai Jiao Tong University); Zhiye Yang (Alibaba Group); Zhizhuang Xing (Alibaba Group); Fei Pan (Alibaba Group); Lvyin Niu (Alibaba Group); FAN WU (Shanghai Jiao Tong University); Haiyang Xu (Alibaba Group); Chuan Yu (Alibaba Group); Yuning Jiang (Alibaba Group); Xiaoqiang Zhu (Alibaba Group)