资源分享 | WSDM2020推荐系统论文打包下载

前言

在今年2月份召开的WSDM是检索和推荐领域的重要会议，虽然只是CCF定义的B类会议，但是却也是推荐方向研究者需要重点关注的。之前分享的一篇关于利用对抗技术来权衡推荐精度与用户隐私的文章就出自于WSDM2020。2021年的WSDM又开始征集稿件了，为了更好的展望未来，我们立足于过去，对2020年WSDM的论文接收列表进行了分析，如下图所示

持续关注我们会议聚焦的同学们应该能够看出，WSDM2020的关键词的热度分布和上周分享的SIGIR2020推荐系统论文聚焦如出一辙，推荐（Recommendation）所占的比重是最大的，使用图与网络（Graph/Network）的方式进行数据进行组织，再通过神经网络（Neural）学习出用户（User）的表示（Embedding）。怎么样，是不是熟悉的配方熟悉的味道呢？

推荐论文列表

1. Addressing Marketing Bias in Product Recommendations.  Mengting Wan, Jianmo Ni (University of California San Diego, United States); Rishabh Misra (Twitter, United States); Julian McAuley (University of California San Diego, United States).

2. ADMM SLIM: Sparse Recommendations for Many Users.  Harald Steck, Maria Dimakopoulou, Nickolai Riabov (Netflix, United States); Tony Jebara (Spotify & Columbia University, United States).

3. Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks. Ruirui Li (University of California Los Angeles, United States); Xian Wu (University of Notre Dame, United States); Wei Wang (University of California Los Angeles, United States).

4. Consistency-Aware Recommendation for User-Generated Item List Continuation. Yun He, Yin Zhang (Texas A&M University, United States); Weiwen Liu (The Chinese University of Hong Kong); James Caverlee (Texas A&M University, United States).

5. DDTCDR: Deep Dual Transfer Cross Domain Recommendation.  Pan Li, Alexander Tuzhilin (New York University, United States).

6. Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation.   Yuan Zhang, Xiaoran Xu, Hanning Zhou, Yan Zhang (Peking University, China).

7. End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding.  Feng Liu (Harbin Institute of Technology, China); Huifeng Guo (Huawei, China); Xutao Li (Harbin Institute of Technology, China); Ruiming Tang (Huawei, China); Yunming Ye (Harbin Institute of Technology, China); Xiuqiang He (Huawei, China).

8. Key Opinon Leaders in Recommendation Systems: Opinion Elicitation and Diffusion.    Jianling Wang (Texas A&M University, United States); Kaize Ding (Arizona State University, United States); Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

9. LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation.   Changfeng Sun, Han Liu, Meng Liu, Zhaochun Ren, Tian Gan, Liqiang Nie (Shandong University, China).

10. Learning a Joint Search and Recommendation Model from User-Item Interactions.    Hamed Zamani, Bruce Croft (University of Massachusetts Amherst, United States).

11. Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning.  Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu (Arizona State University, United States).

12. Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation.    Lixin Zou (Tsinghua University, China); Long Xia (JD.com, China); Pan Du (University of Montreal, Canada); Zhuo Zhang (The University of Melbourne, Australia); Ting Bai (Renmin University of China, China); Weidong Liu (Tsinghua University, China); Jian-Yun Nie (University of Montreal, Canada); Dawei Yin (JD.com, China).

13. RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback.    Ilya Shenbin, Anton Alekseev, Elena Tutubalina (Steklov Institute of Mathematics at St. Petersburg, Russia); Valentin Malykh (Moscow Institute of Physics and Technology, Russia); Sergey Nikolenko (Steklov Institute of Mathematics at St. Petersburg, Russia).

14. Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation. Nengjun Zhu, Jian Cao (Shanghai Jiao Tong University, China); Yanchi Liu (Rutgers University, United States); Yang Yang (Nanjing University, China); Haochao Ying (Zhejiang University, China); Hui Xiong (Rutgers University, United States).

15. Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling.   Jiarui Qin (Shanghai Jiao Tong University, China); Kan Ren (Microsoft, China); Yuchen Fang, Weinan Zhang, Yong Yu (Shanghai Jiao Tong University, China).

16. Time Interval Aware Self-Attention for Sequential Recommendation.  Jiacheng Li (University of California San Diego, United States); Yujie Wang (Florida State University, United States); Julian McAuley (University of California San Diego, United States)

17. Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce.  Jianling Wang (Texas A&M University, United States); Raphael Louca, Diane Hu, Caitlin Cellier (Etsy Inc., United States); James Caverlee (Texas A&M University, United States); Liangjie Hong (Etsy Inc., United States).

18. User Recommendation in Content Curation Platforms.  Jianling Wang, Ziwei Zhu, James Caverlee (Texas A&M University, United States).

19. Hierarchical User Profiling for E-commerce Recommender Systems.  Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

20. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. Lucas Vinh Tran, Yi Tay (Nanyang Technological University, Singapore); Shuai Zhang (The University of New South Wales, Australia); Gao Cong (Nanyang Technological University, Singapore); Xiaoli Li (Institute for Infocomm Research, Singapore).

21. Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee (Texas A&M University, United States).

22. PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems.   Azin Ghazimatin (Max Planck Institute for Informatics, Germany); Oana Balalau (Inria and École Polytechnique); Rishiraj Saha Roy, Gerhard Weikum (Max Plank Institute for Informatics, Germany).

23. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. Yuta Saito (Tokyo Institute of Technology, Japan); Suguru Yaginuma, Yuta Nishino, Hayato Sakata (SMN Corporation, Japan); Kazuhide Nakata (Tokyo Institute of Technology, Japan).

24. Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems. Wenqiang Lei (National University of Singapore, Singapore); Xiangnan He (University of Science and Technology of China, China); Yisong Miao (National University of Singapore, Singapore); Qingyun Wu (University of virginia, United States); Richang Hong (Hefei University of Technology, China); Min-Yen Kan, Tat Seng Chua (National University of Singapore, Singapore).