Recent literatures explore the intersection of natural language processing and recommender systems.

This is a collection of research papers on this topic. The Papers are sorted by time. Any suggestions and pull requests are welcome.

• Review Papers
• Research Papers
  • KG for Recommendation
  • Text Ad Generation
  • Conversational Recommendation
  • Explainable Recommendation
  • Text Recommendation
  • Context-aware Recommendation

• Critiquing-based recommenders: survey and emerging trends. Li Chen, Pearl Pu. UMUAI 2012.
• Explainable Recommendation: A Survey and New Perspectives. Yongfeng Zhang, Xu Chen. 2018.

• Personalized Entity Recommendation: A Heterogeneous Information Network Approach. Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han. WSDM 2014. UIUC.
• Collaborative Knowledge Base Embedding for Recommender Systems. Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, Wei-Ying Ma. KDD 2016. Microsoft Research.
• Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lun Lee. KDD 2017.
• Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. SIGIR 2018.
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo. CIKM 2018. SJTU.
• Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo. WWW 2019. SJTU.
• Jointly Learning Explainable Rules for Recommendation with Knowledge Graph. Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren. WWW 2019.
• KGAT: Knowledge Graph Attention Network for Recommendation. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KDD 2019. NUS.
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. SIGIR 2019.

• Neural Rating Regression with Abstractive Tips Generation for Recommendation. Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, Wai Lam. SIGIR 2017. CUHK.
• Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning. John Hughes, Keng-Hao Chang and Ruofei Zhang. KDD 2019. Microsoft.
• Towards Knowledge-Based Personalized Product Description Generation in E-commerce. Qibin Chen*, Junyang Lin*, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang. KDD 2019. Alibaba.
• Long and Diverse Text Generation with Planning-based Hierarchical Variational Model. Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, Xiaoyan Zhu. EMNLP 2019. Tsinghua.
• Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce. Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu. NAACL-HLT 2019. Alibaba.

• Deep Dialogue vs Casual Conversation in Recommender Systems. Lorraine McGinty, Barry Smyth. 2002.
• A Personalized System for Conversational Recommendations. Cynthia A. Thompson, Mehmet H. Göker, Pat Langley. JAIR 2004.
• Improving Recommender Systems with Adaptive Conversational Strategies. Tariq Mahmood, Francesco Ricci. HT 2009.
• Critiquing-based recommenders: survey and emerging trends. Li Chen, Pearl Pu. UMUAI 2012.
• Conversational Recommendation to Avoid the Cold-start Problem. F. Benito-Picazo, M. Enciso, C. Rossi and A. Guevara. CMMSE 2016.
• Towards Conversational Recommender Systems. Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann. KDD 2016. Microsoft.
• Conversational Recommender System. Yueming Sun, Yi Zhang. SIGIR 2018. UCSC.
• Towards Deep Conversational Recommendations. Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. NeurIPS 2018. Element AI.
• Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems. Claudio Greco, Alessandro Suglia, Pierpaolo Basile, and Giovanni Semeraro. AIIA 2019.
• Towards Knowledge-Based Recommender Dialog System. Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang. EMNLP 2019. Alibaba.
• Deep Conversational Recommender in Travel. Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua. arXiv preprint. NUS.

• Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. Yongfeng Zhang,Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu,Shaoping Ma. SIGIR 2014. Tsinghua.
• Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems. Beidou Wang, Martin Ester, Jiajun Bu, Deng Cai. AAAI 2014. ZJU.
• TriRank: Review-aware Explainable Recommendation by Modeling Aspects. Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen. CIKM 2015. NUS.
• Crowd-Based Personalized Natural Language Explanations for Recommendations. Shuo Chang, F. Maxwell Harper, Loren Terveen. RecSys 2016.
• Social Collaborative Viewpoint Regression with Explainable Recommendations. Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, Maarten de Rijke. WSDM 2017.
• Explainable Entity-based Recommendations with Knowledge Graphs. Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen. RecSys 2017.
• Why I like it: Multi-task Learning for Recommendation and Explanation. Yichao Lu, Ruihai Dong, Barry Smyth. RecSys 2018.
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation. Xiang Wang, Xiangnan He, Xiangnan He, Liqiang Nie, Tat-Seng Chua. WWW 2018. NUS.
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. Algorithms 2018.
• Explainable Recommendation: A Survey and New Perspectives. Yongfeng Zhang, Xu Chen. 2018.
• Explainable Recommendation Through Attentive Multi-View Learning. Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie. AAAI 2019. Microsoft Research Asia.
• Explainable Reasoning over Knowledge Graphs for Recommendation. Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua. AAAI 2019. NUS.
• A Reinforcement Learning Framework for Explainable Recommendation. Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, Xing Xie. ICDM 2018. Microsoft Research Asia.
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. SIGIR 2019.

• Ask the GRU: Multi-Task Learning for Deep Text Recommendations. Trapit Bansal, David Belanger, Andrew McCallum. RecSys 2016.
• Embedding-based News Recommendation for Millions of Users. Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. KDD 2017.
• DKN: Deep Knowledge-Aware Network for News Recommendation. Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo. WWW 2018.
• DRN: A Deep Reinforcement Learning Framework for News Recommendation. Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, Zhenhui Li. WWW 2018.
• Neural News Recommendation with Long- and Short-term User Representations. Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie. ACL 2019.
• NPA: Neural News Recommendation with Personalized Attention. Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie. KDD 2019.
• Neural News Recommendation with Attentive Multi-View Learning. Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie. IJCAI 2019.

• Cross-domain Collaboration Recommendation. Jie Tang, Sen Wu, Jimeng Sun, Hang Su. KDD 2012.
• A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. Ali Elkahky, Yang Song, Xiaodong He. WWW 2015. Microsoft Research.
• Deep Neural Networks for YouTube Recommendations. Paul Covington, Jay Adams, Emre Sargin. RecSys 2016. Google.
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation. Lei Zheng, Vahid Noroozi, Philip S. Yu. WSDM 2017. UIUC.