KDD 2020关于深度推荐系统与CTR预估工业界必读的论文

导读：本文主要简要列举下Google、Tencent、Alibaba以及Huawei等各大公司工业界在KDD 2020上关于深度推荐系统与CTR预估相关的论文。

1. Multitask Mixture of Sequential Experts for User Activity Streams, Google
Authors: Yicheng Cheng: Google; Zhen Qin: Google Inc.; Zhe Zhao: Google; Zhe Chen: Google; Donald Metzler: Google; Jingzheng Qin: Google

论文：https://research.google/

pubs/pub49274/

2. Neural Input Search for Large Scale Recommendation Models, Google

Authors: Manas Joglekar: Not Available; Cong Li: Google; Mei Chen: Google; Taibai Xu: Google; Xiaoming Wang: Google; Jay Adams: Google; Pranav Khaitan: Google; Jiahui Liu: Google; Quoc Le: Google

论文：https://arxiv.org/abs/1907.04471

3. Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems, Google

Authors: Hao-Jun Shi: Northwestern University; Dheevatsa Mudigere: Facebook; Maxim Naumov: Facebook; Jiyan Yang: Facebook

论文：https://arxiv.org/abs/1909.02107

4. Embedding-based Retrieval in Facebook Search, Facebook
Authors: Jui-Ting Huang: Facebook; Ashish Sharma: Facebook; Shuying Sun: Facebook; Li Xia: Facebook; David Zhang: Facebook; Philip Pronin: Facebook; Janani Padmanabhan: Facebook; Giuseppe Ottaviano: Facebook; Linjun Yang: Facebook

论文：https://arxiv.org/abs/2006.11632

5. GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce, Facebook
Authors: Sean Bell: Facebook; Yiqun Liu: Facebook; Sami Alsheikh: Facebook; Yina Tang: Facebook; Ed Pizzi: Facebook; Michael Henning: Facebook; Karun Singh: Facebook; Omkar Parkhi: Facebook; Fedor Borisyuk: Facebook

论文：http://t.cn/A6LrnQdy

6. TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook, Facebook
Authors: Nima Noorshams: FACEBOOK; Saurabh Verma: FACEBOOK; Aude Hofleitner: FACEBOOK

论文：https://arxiv.org/abs/2002.07917

7. General-Purpose User Embeddings based on Mobile App Usage, Tencent

Authors: Junqi Zhang: Tencent; Bing Bai: Tencent; Ye Lin: Tencent; Jian Liang: Tencent; Kun Bai: Tencent; Fei Wang: Cornell University

论文：https://arxiv.org/abs/2005.13303

8. Understanding Negative Sampling in Graph Representation Learning, Alibaba
Authors: Zhen Yang: Department of Computer Science and Technology, Tsinghua University; Ming Ding: Department of Computer Science and Technology, Tsinghua University; Chang Zhou: DAMO Academy, Alibaba Group; Hongxia Yang: DAMO Academy, Alibaba Group; Jingren Zhou: DAMO Academy, Alibaba Group; Jie Tang: Department of Computer Science and Technology, Tsinghua University

论文：https://arxiv.org/abs/2005.09863

9. Controllable Multi-Interest Framework for Recommendation, Alibaba
Authors: Yukuo Cen: Tsinghua University; Jianwei Zhang: Alibaba Group; Xu Zou: Tsinghua University; Chang Zhou: Alibaba Group; Hongxia Yang: Alibaba Group; Jie Tang: Tsinghua University

论文：https://arxiv.org/abs/2005.09347

代码：https://github.com/

THUDM/ComiRec

10. M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems, Alibaba
Authors: Menghan Wang: Alibaba Group; Yujie Lin: Alibaba; Guli Lin: Alibaba; Keping Yang: Alibaba; Xiaoming Wu: Hong Kong Polytechnic University

论文：https://arxiv.org/abs/2005.10110

代码：https://github.com/99731/M2GRL

11. Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective, Alibaba
Authors: Yifei Zhao: Alibaba; Yu-Hang Zhou: Alibaba; Mingdong Ou: Alibaba

论文：https://arxiv.org/abs/2006.04520

12. Privileged Features Distillation at Taobao Recommendations, Alibaba
Authors: Chen Xu: Alibaba Inc; Quan Li: Alibaba Inc; Junfeng Ge: Alibaba Group; Jinyang Gao: Alibaba; Xiaoyong Yang: Alibaba Group; Changhua Pei: Tsinghua University; Fei Sun: Alibaba Inc; Jian Wu: Alibaba Inc; Hanxiao Sun: Alibaba Group; Wenwu Ou: Alibaba Inc

论文：https://arxiv.org/abs/1907.05171

13. AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction, Huawei
Authors: Bin Liu: ByteDance; Chenxu Zhu: Shanghai Jiao Tong University; Guilin Li: Noah s Ark Lab Huawei ; Weinan Zhang: Shanghai Jiao Tong University; Jincai Lai: Noah s Ark Lab Huawei ; Ruiming Tang: Noah s Ark Lab Huawei ; Xiuqiang He: Noah s Ark Lab Huawei ; Zhengguo Li: Noah s Ark Lab Huawei ; Yong Yu: Shanghai Jiao Tong University

论文：https://arxiv.org/abs/2003.11235

代码：https://github.com/

zhuchenxv/AutoFIS

14. On Sampled Metrics for Item Recommendation, Google

Authors: Walid Krichene: Google; Steffen Rendle: Google

15. Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies, Google

Authors: Zhen Qin: Google; Suming J. Chen: University of California Los Angeles ; Donald Metzler: Google; Yongwoo Noh: Google; Jingzheng Qin: Google; Xuanhui Wang: Google

16. Grale: Designing Networks for Graph Learning, Google

Authors: Jonathan Halcrow: Google; Alexandru Mo_oi: Google; Sam Ruth: Google; Bryan Perozzi: Google

17. Improving Recommendation Quality in Google Drive, Google
Authors: Suming J. Chen: Google; Zhen Qin: Google; Zachary Wilson: Google; Brian Calaci: Google; Michael Rose: Google; Ryan Evans: Google; Sean Abraham: Google; Donald Metzler: Google; Sandeep Tata: Google; Mike Colagrosso: Google

18. Scaling Graph Neural Networks with Approximate PageRank, Google

Authors: Aleksandar Bojchevski: Technical University of Munich; Johannes Klicpera: Technical University of Munich; Bryan Perozzi: Google; Amol Kapoor: Google; Martin Blais: Google; Benedek Rozemberczki: The University of Edinburgh; Michal Lukasik: Google; Stephan Günnemann: Technical University of Munich

19. Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction, Facebook
Authors: Qingquan Song: Texas A&M University; Dehua Cheng: Facebook Inc.; Eric Zhou: Facebook Inc.; Jiyan Yang: Facebook Inc.; Yuandong Tian: Facebook Inc.; Xia Hu: Texas A&M University

20. A Request-level Guaranteed Delivery Advertising Planning: Forecasting and Allocation, Tencent
Authors: Hong Zhang: Tencent; Lan Zhang: University of Science of Technology of China; Lan Xu: Tencent; Xiaoyang Ma: Tencent; Zhengtao Wu: University of Science of Technology of China; Cong Tang: University of Science of Technology of China; Wei Xu: Tencent; Yiguo Yang: Tencent

21. Meta-Learning for Query Conceptualization at Web Scale, Tencent
Authors: Fred X. Han: University of Alberta; Di Niu: University of Alberta; Haolan Chen: Tencent; Weidong Guo: Tencent; Shengli Yan: Tencent; Bowei Long: Tencent

22. Attention and Memory-Augmented Networks for Dual-View Sequential Learning, Alibaba
Authors: Yong He: Alibaba; Cheng Wang: Alibaba; Nan Li: Alibaba; Zhenyu Zeng: Alibaba

23. Disentangled Self-Supervision in Sequential Recommenders, Alibaba
Authors: Jianxin Ma: Alibaba Group; Tsinghua University; Chang Zhou: Alibaba Group; Hongxia Yang: Alibaba Group; Cui Peng: Tsinghua University; Xin Wang: Tsinghua University; Wenwu Zhu: Tsinghua University

24. Large-Scale Training System for 100-Million Classification at Alibaba, Alibaba
Authors: Liuyihan Song: Alibaba Group; Pan Pan: Alibaba Group; Kang Zhao: Alibaba Group; Hao Yang: Alibaba Group; Yiming Chen: Alibaba Group; Yingya Zhang: Alibaba Group; Yinghui Xu: Alibaba Group; Rong Jin: Alibaba Group

25. Multi-objective Optimization for Guaranteed Delivery in Video Service Platform, Alibaba
Authors: Hang Lei: Alibaba Group; Yin Zhao: Alibaba Group; Longjun Cai: Alibaba Group

26. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks, Huawei
Authors: Jianing Sun: Huawei Technologies Canada; Wei Guo: Huawei Noah's Ark Lab; Dengcheng Zhang: Huawei Distributed and Parallel Software Lab; Yingxue Zhang: Huawei Technologies Canada; Florence Robert-Regol: McGill University; Yaochen Hu: Huawei Technologies Canada; Huifeng Guo: Huawei Noah's Ark Lab; Ruiming Tang: Huawei Noah's Ark Lab; Han Yuan: Huawei Distributed and Parallel Software Lab; Xiuqiang He: Huawei Noah's Ark Lab; Mark Coates: McGill University

27. Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation, Huawei
Authors: Chen Ma: McGill University; Liheng Ma: McGill University; Yingxue Zhang: Huawei Technologies Canada; Ruiming Tang: Huawei Noah's Ark Lab; Xue Liu: McGill University; Mark Coates: McGill University

更多WSDM 2020 accepted paper list请点击文末左下角原文链接查看。本文中涉及到的所有论文以及更多最前沿的推荐广告方面的论文分享请移步如下的GitHub项目进行学习交流、star以及fork，后续仓库会持续更新最新论文。

https://github.com/imsheridan/DeepRec

推荐阅读

这个NLP工具，玩得根本停不下来

文本自动摘要任务的“不完全”心得总结番外篇——submodular函数优化

Node2Vec 论文+代码笔记

模型压缩实践收尾篇——模型蒸馏以及其他一些技巧实践小结

中文命名实体识别工具（NER）哪家强？

学自然语言处理，其实更应该学好英语

斯坦福大学NLP组Python深度学习自然语言处理工具Stanza试用

关于AINLP

AINLP 是一个有趣有AI的自然语言处理社区，专注于 AI、NLP、机器学习、深度学习、推荐算法等相关技术的分享，主题包括文本摘要、智能问答、聊天机器人、机器翻译、自动生成、知识图谱、预训练模型、推荐系统、计算广告、招聘信息、求职经验分享等，欢迎关注！加技术交流群请添加AINLPer(id：ainlper)，备注工作/研究方向+加群目的。

阅读至此了，分享、点赞、在看三选一吧🙏