AAAI 2020关于深度推荐系统与CTR预估相关的论文

导读：本文是“深度推荐系统”专栏的第十六篇文章，这个系列将介绍在深度学习的强力驱动下，给推荐系统工业界所带来的最前沿的变化。本文主要简要列举下Google、Facebook、Alibaba以及Tencent等各大公司在AAAI 2020上关于深度推荐系统与CTR预估相关的论文。

1. Deep Match to Rank Model for Personalized Click-Through Rate Prediction Ze Lyu (Alibaba Group)*; Yu Dong (Alibaba Group); Chengfu Huo (Alibaba Group); Weijun Ren (Alibaba Group)

2. Pairwise Fairness for Ranking and Regression

Harikrishna Narasimhan (Google)*; Andrew Cotter (Google); Maya Gupta (Google); Serena Wang (Google)

3. Diversified Interactive Recommendation with Implicit Feedback

Yong Liu(Nanyang Technological University)*; Yingtai Xiao (University of Science and Technology of China); Qiong Wu (Nanyang Technological University); Chunyan Miao (NTU); Juyong Zhang (University of Science and Technology of China); Binqiang Zhao (Alibaba); Haihong Tang (Alibaba Group)

4. Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution

Shu-ting Shi (Nanjing University); Wenhao Zheng (Nanjing University); Jun Tang (Alibaba Group); Qing-Guo Chen (Alibaba); Yao Hu (Alibaba Youku Cognitive and Intelligent Lab); Jianke Zhu (Zhejiang University); Ming Li (Nanjing University)*

5. Memory Augmented Graph Neural Networks for Sequential Recommendation

Chen Ma (McGill University)*; Liheng Ma (McGill University); Yingxue Zhang (Huawei Technologies Canada); Jianing Sun (Huawei Technologies Canada); Xue Liu (McGill University); Mark Coates (McGill University)

6. Distributed Primal-Dual Optimization for Online Multi-Task Learning

Peng Yang (Baidu USA)*; Ping Li (Baidu)

7. PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Tiancheng Shen (Tsinghua University); Jia Jia (Tsinghua University)*; Yan Li (Tencent WeChat); Yihui Ma (Tsinghua University); Yaohua Bu (Tsinghua University); Hanjie Wang (Tencent WeChat); Bo Chen (Tencent WeChat); TatSeng Chua (National university of Singapore); Wendy Hall (University of Southampton)

8. Question-driven Purchasing Propensity Analysis for Recommendation

Long Chen (Xi’an University of Posts & Telecommunications)*; Qibin Xu (Zhejiang University); Ziyu Guan (Xidian University); Qiong Zhang (Alibaba Group); Huan Sun (Ohio State University); Guangyue Lu (Xian University of Posts and Telecommunications); Deng Cai (ZJU)

9. Leveraging Title-Abstract Attentive Semantics for Paper Recommendation

Guibing Guo (Northeastern University)*; Bowei Chen (Northeastern University); Xiaoyan Zhang (College of Computer Science and Software Engineering, Shenzhen University); Zhirong Liu (Huawei Noah's Ark Lab); Zhenhua Dong (Huawei Noah's Ark Lab); Xiuqiang He (Huawei Noah's Ark Lab)

10. Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests

Xiao Xu (Cornell University); Fang Dong (Alibaba Group)*; Yanghua Li (Alibaba Group); Shaojian He (Alibaba Group); Xin Li (Alibaba Group)

11. Improved Algorithms for Conservative Exploration in Bandits

Evrard Garcelon (Facebook)*; Mohammad Ghavamzadeh (Facebook); Alessandro Lazaric (FAIR); Matteo Pirotta (Facebook)

12. Linear Bandits with Feature Feedback

Urvashi Oswal (Oracle Labs)*; Aniruddha Bhargava (Amazon.com, Inc.); Robert Nowak (University of Wisconsin, Madison)

13. Knowledge Distillation from Internal Representations

Gustavo Aguilar (University of Houston)*; Yuan Ling (Amazon); Yu Zhang (Amazon); Benjamin Yao (Amazon); Xing Fan (Amazon); Edward Guo (Amazon)

14. Social Influence Does Matter: User Action Prediction for In-Feed Advertising

Hongyang Wang (Renmin University of China); Qingfei Meng (Renmin University of China ); Ju Fan (Renmin University of China)*; Yuchen Li (Singapore Management University); Laizhong Cui (Shenzhen University); Xiaoman Zhao (Renmin University of China); Chong Peng (Tencent); Gong Chen (Tencent); Xiaoyong Du (Renmin University of China)

15. Count-Based Exploration with the Successor RepresentationMarlos C. Machado (Google Brain)*; Marc G. Bellemare (Google Brain); Michael Bowling (University of Alberta)

16. Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Taihong Xiao (University of California at Merced)*; Yi-Hsuan Tsai (NEC Labs America); Kihyuk Sohn (Google); Manmohan Chandraker (NEC Labs America); Ming-Hsuan Yang (University of California at Merced)

17. Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network

Zhe Ma (Zhejiang University ); Jianfeng Dong (Zhejiang Gongshang University)*; Zhongzi Long (Zhejiang University); Yao Zhang (Zhejiang University); Yuan He (Alibaba Group ); hui xue (Alibaba); Shouling Ji (Zhejiang University)

18. Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

Wenlin Wang (Duke Univeristy)*; Hongteng Xu (Duke University); Zhe Gan (Microsoft); Bai Li (Duke University); Guoyin Wang (Duke University); Liqun Chen (Duke University); Qian Yang (Duke University); Wenqi Wang (Facebook); Lawrence Carin Duke (CS)

19. Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning

Yingru Liu (Stony Brook University); Xuewen Yang (Stony Brook University); Dongliang Xie (Beijing University of Posts and Telecommunications)*; Xin Wang (Department of Electrical and Computer Engineering, State of New York University at Stony Brook); Li Shen (Tencent AI Lab); Hao-Zhi Huang (Tencent AI Lab); Niranjan Balasubramanian (stony brook)

20. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Weiqi Chen (Zhejiang University); Ling Chen (Zhejiang University)*; Yu Xie (Alibaba Cloud); Wei Cao (Alibaba); Yusong Gao (Alibaba Cloud); Xiaojie Feng (Alibaba Cloud)

21. Who Likes What? – SplitLBI in Exploring Preferential Diversity of Ratings

Qianqian Xu (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences)*; Jiechao Xiong (Tencent AI Lab); Zhiyong Yang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences); Xiaochun Cao (Chinese Academy of Sciences); Qingming Huang (University of Chinese Academy of Sciences); Yuan Yao (HongKong University of Science and Technology)

22. Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

Deli Chen (Peking University)*; Yankai Lin (Wechat Tencent); Wei Li (Peking University); Peng Li (Wechat Tencent); Jie Zhou (Tencent); Xu Sun (Peking University)

本文中涉及到的重要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