嘿，记得给“机器学习与推荐算法”添加星标

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）。

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)

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)