KDD2021推荐系统论文集锦

嘿，记得给“机器学习与推荐算法”添加星标

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一年一度的知识发现与数据挖掘顶级会议SIGKDD将于8月14日至18日在线上举行。据统计，今年共有1541篇有效投稿，其中238篇论文被接收，接收率为15.44%，相比KDD2020的接收率16.8%有所下降。其中，涉及到的推荐系统相关的论文共38篇（包括Research Track和Applied Data Science Track），相比于去年的32篇有所增加KDD2020推荐系统论文聚焦（注：本文涉及的推荐系统相关论文的整理很可能具有极强的个人倾向，因此请勿抬杠。另外，整理不易，欢迎小手点个在看/分享）。

本公众号一如既往的收集与整理了发表在该会议上的推荐系统相关论文，以供研究者与工程师们提前一睹为快。本会议接受的论文主要分为了Research Track Papers与Applied Data Science Track Papers，因此大家可以即关注学术界的最新动态，也可以学习业界贴合真实场景的技巧。如果不放心本文整理的推荐系统论文集锦，也可自行前往官网查看，官网接收论文列表如下：

https://kdd.org/kdd2021/accepted-papers/index

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Research Track Papers

研究赛道的论文主要是按照推荐子领域来划分，比如推荐系统中的隐私与安全、推荐系统中的偏置、推荐系统与边缘计算结合、基于自监督的推荐系统、基于知识蒸馏的推荐系统、冷启动问题、协同过滤问题、推荐效率问题等。

[1] 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)

[2] 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)

[3] 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.)

[4] Efficient Data-specific Model Search for Collaborative Filtering

Authors: Chen Gao (Tsinghua University)*; Quanming Yao (4Paradigm); Depeng Jin (Tsinghua University); Yong Li (Tsinghua University)

[5] 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)

[6] 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)

[7] 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)

[8] 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)

[9] 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)

[10] 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)

[11] 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)

[12] 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)

[13] Preference Amplification in Recommender Systems

Authors: Dimitris Kalimeris (Harvard); Smriti Bhagat (Facebook)*; Shankar Kalyanaraman (Facebook); Udi Weinsberg (Facebook)

[14] 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)

[15] 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)

[16] 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)

[17] Topology Distillation for Recommender System

Authors: SeongKu Kang (POSTECH)*; Junyoung Hwang (POSTECH); Wonbin Kweon (POSTECH); Hwanjo Yu (POSTECH)

[18] 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)

[19] 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)

[20] 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)

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Applied Data Science Track Papers

[1] 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)

[2] 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)

[3] 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)

[4] 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)

[5] 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)

[6] 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)

[7] 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)

[8] 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)

[9] 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)

[10] 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)

[11] 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)

[12] 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)

[13] 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.)

[14] 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)

[15] Sliding Spectrum Decomposition for Diversified Recommendation

Authors: Yanhua Huang (Xiaohongshu)*; Weikun Wang (Xiaohongshu); Lei Zhang (Xiaohongshu); Ruiwen Xu (Xiaohongshu)

[16] 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)

[17] 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")

[18] 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)

更多论文，欢迎访问https://github.com/hongleizhang/RSPapers

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