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https://github.com/thudm/nlp4rec-papers

Paper list of NLP for recommender systems
https://github.com/thudm/nlp4rec-papers

dialogue-systems explainable-recommendation knowledge-graph natural-language-processing recommender-system

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Paper list of NLP for recommender systems

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# Paper Collection of NLP for Recommender System



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.



## Overview



* [Review Papers](https://github.com/THUDM/NLP4Rec-Papers#review-papers)

* [Research Papers](https://github.com/THUDM/NLP4Rec-Papers#research-papers)

  * [KG for Recommendation](https://github.com/THUDM/NLP4Rec-Papers#kg-for-recommendation)

  * [Text Ad Generation](https://github.com/THUDM/NLP4Rec-Papers#text-ad-generation)

  * [Conversational Recommendation](https://github.com/THUDM/NLP4Rec-Papers#conversational-recommendation)

  * [Explainable Recommendation](https://github.com/THUDM/NLP4Rec-Papers#explainable-recommendation)

  * [Text Recommendation](https://github.com/THUDM/NLP4Rec-Papers#text-recommendation)

  * [Context-aware Recommendation](https://github.com/THUDM/NLP4Rec-Papers#context-aware-recommendation)



## Review Papers



* [Critiquing-based recommenders: survey and emerging trends](https://link.springer.com/content/pdf/10.1007/s11257-011-9108-6.pdf). Li Chen, Pearl Pu. UMUAI 2012.

* [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192). Yongfeng Zhang, Xu Chen. 2018.



## Research Papers



### KG for Recommendation



* [Personalized Entity Recommendation: A Heterogeneous Information Network Approach](https://www.cse.cuhk.edu.hk/irwin.king/_media/presentations/wsdm14_xyu.pdf). 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](https://www.kdd.org/kdd2016/papers/files/adf0066-zhangA.pdf). 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](https://www.researchgate.net/profile/Quanming_Yao/publication/317523407_Meta-Graph_Based_Recommendation_Fusion_over_Heterogeneous_Information_Networks/links/59eb9c264585151983cb73ff/Meta-Graph-Based-Recommendation-Fusion-over-Heterogeneous-Information-Networks.pdf). Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lun Lee. KDD 2017.

* [Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks](https://dl.acm.org/citation.cfm?id=3210017). 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](https://arxiv.org/pdf/1803.03467). 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](https://arxiv.org/pdf/1901.08907). Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo. WWW 2019. SJTU.

* [Jointly Learning Explainable Rules for Recommendation with Knowledge Graph](https://arxiv.org/pdf/1903.03714). 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](https://arxiv.org/pdf/1905.07854). Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KDD 2019. NUS.

* [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://arxiv.org/pdf/1906.05237). Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. SIGIR 2019.



### Text Ad Generation



* [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://arxiv.org/pdf/1708.00154). Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, Wai Lam. SIGIR 2017. CUHK.

* [Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning](https://www.kdd.org/kdd2019/accepted-papers/view/generating-better-search-engine-text-advertisements-with-deep-reinforcement). John Hughes, Keng-Hao Chang and Ruofei Zhang. KDD 2019. Microsoft.

* [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://arxiv.org/abs/1903.12457). 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](https://arxiv.org/abs/1908.06605). 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](https://arxiv.org/abs/1904.01735). Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu. NAACL-HLT 2019. Alibaba.



### Conversational Recommendation



* [Deep Dialogue vs Casual Conversation in Recommender Systems](https://pdfs.semanticscholar.org/6510/e2b27354d281df176860715beb82b7798fab.pdf?_ga=2.158968378.1807733050.1571751313-1792050006.1563377190). Lorraine McGinty, Barry Smyth. 2002.

* [A Personalized System for Conversational Recommendations](https://www.jair.org/index.php/jair/article/download/10374/24832). Cynthia A. Thompson, Mehmet H. Göker, Pat Langley. JAIR 2004.

* [Improving Recommender Systems with Adaptive Conversational Strategies](https://www.researchgate.net/profile/Francesco_Ricci5/publication/221267362_Improving_recommender_systems_with_adaptive_conversational_strategies/links/0deec5232cff38507f000000/Improving-recommender-systems-with-adaptive-conversational-strategies.pdf). Tariq Mahmood, Francesco Ricci. HT 2009.

* [Critiquing-based recommenders: survey and emerging trends](https://link.springer.com/content/pdf/10.1007/s11257-011-9108-6.pdf). Li Chen, Pearl Pu. UMUAI 2012.

* [Conversational Recommendation to Avoid the Cold-start Problem](https://www.researchgate.net/profile/Fernando_Benito-Picazo/publication/304895711_Conversational_recommendation_to_avoid_the_cold-start_problem/links/577ccb5508aec3b74337b2d9/Conversational-recommendation-to-avoid-the-cold-start-problem.pdf). F. Benito-Picazo, M. Enciso, C. Rossi and A. Guevara. CMMSE 2016.

* [Towards Conversational Recommender Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf). Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann. KDD 2016. Microsoft.

* [Conversational Recommender System](https://arxiv.org/pdf/1806.03277). Yueming Sun, Yi Zhang. SIGIR 2018. UCSC.

* [Towards Deep Conversational Recommendations](https://arxiv.org/pdf/1812.07617). 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](https://www.researchgate.net/profile/Alessandro_Suglia/publication/320875588_Converse-Et-Impera_Exploiting_Deep_Learning_and_Hierarchical_Reinforcement_Learning_for_Conversational_Recommender_Systems/links/5bf6ad1592851c6b27d27324/Converse-Et-Impera-Exploiting-Deep-Learning-and-Hierarchical-Reinforcement-Learning-for-Conversational-Recommender-Systems.pdf). Claudio Greco, Alessandro Suglia, Pierpaolo Basile, and Giovanni Semeraro. AIIA 2019.

* [Towards Knowledge-Based Recommender Dialog System](https://arxiv.org/abs/1908.05391). Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang. EMNLP 2019. Alibaba.

* [Deep Conversational Recommender in Travel](https://arxiv.org/pdf/1907.00710). Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua. arXiv preprint. NUS.



### Explainable Recommendation



* [Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis](https://www.cs.cmu.edu/~glai1/papers/yongfeng-guokun-sigir14.pdf). 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](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPDFInterstitial/8155/8417). Beidou Wang, Martin Ester, Jiajun Bu, Deng Cai. AAAI 2014. ZJU.

* [TriRank: Review-aware Explainable Recommendation by Modeling Aspects](http://www.cs.jhu.edu/~taochen/data/pubs/cikm15.pdf). Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen. CIKM 2015. NUS.

* [Crowd-Based Personalized Natural Language Explanations for Recommendations](https://dl.acm.org/citation.cfm?id=2959153). Shuo Chang, F. Maxwell Harper, Loren Terveen. RecSys 2016.

* [Social Collaborative Viewpoint Regression with Explainable Recommendations](https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/ren-social-2017.pdf). Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, Maarten de Rijke. WSDM 2017.

* [Explainable Entity-based Recommendations with Knowledge Graphs](https://arxiv.org/abs/1707.05254). Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen. RecSys 2017.

* [Why I like it: Multi-task Learning for Recommendation and Explanation](https://www.researchgate.net/profile/Ruihai_Dong/publication/327947836_Why_I_like_it_multi-task_learning_for_recommendation_and_explanation/links/5bbdf9af45851572315be8f5/Why-I-like-it-multi-task-learning-for-recommendation-and-explanation.pdf). Yichao Lu, Ruihai Dong, Barry Smyth. RecSys 2018.

* [TEM: Tree-enhanced Embedding Model for Explainable Recommendation](https://www.comp.nus.edu.sg/~xiangnan/papers/www18-tem.pdf). Xiang Wang, Xiangnan He, Xiangnan He, Liqiang Nie, Tat-Seng Chua. WWW 2018. NUS.

* [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://www.mdpi.com/1999-4893/11/9/137). Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. Algorithms 2018.

* [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192). Yongfeng Zhang, Xu Chen. 2018.

* [Explainable Recommendation Through Attentive Multi-View Learning](https://pdfs.semanticscholar.org/5b05/bb1c13f1395d109406c992d9387df8802550.pdf). Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie. AAAI 2019. Microsoft Research Asia.

* [Explainable Reasoning over Knowledge Graphs for Recommendation](https://arxiv.org/abs/1811.04540). Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua. AAAI 2019. NUS.

* [A Reinforcement Learning Framework for Explainable Recommendation](https://ieeexplore.ieee.org/abstract/document/8594883/). Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, Xing Xie. ICDM 2018. Microsoft Research Asia.

* [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://arxiv.org/pdf/1906.05237). Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. SIGIR 2019.



### Text Recommendation



* [Ask the GRU: Multi-Task Learning for Deep Text Recommendations](https://dl.acm.org/ft_gateway.cfm?id=2959180&type=pdf). Trapit Bansal, David Belanger, Andrew McCallum. RecSys 2016.

* [Embedding-based News Recommendation for Millions of Users](https://dl.acm.org/citation.cfm?id=3098108). Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. KDD 2017.

* [DKN: Deep Knowledge-Aware Network for News Recommendation](https://arxiv.org/abs/1801.08284). Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo. WWW 2018.

* [DRN: A Deep Reinforcement Learning Framework for News Recommendation](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf). 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](https://www.aclweb.org/anthology/P19-1033/). Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie. ACL 2019.

* [NPA: Neural News Recommendation with Personalized Attention](https://arxiv.org/abs/1907.05559). Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie.  KDD 2019.

* [Neural News Recommendation with Attentive Multi-View Learning](https://arxiv.org/abs/1907.05576). Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie. IJCAI 2019.



### Context-aware Recommendation



* [Cross-domain Collaboration Recommendation](https://dl.acm.org/citation.cfm?id=3220050). Jie Tang, Sen Wu, Jimeng Sun, Hang Su. KDD 2012.

* [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](http://sonyis.me/paperpdf/frp1159-songA-www-2015.pdf). Ali Elkahky, Yang Song, Xiaodong He. WWW 2015. Microsoft Research.

* [Deep Neural Networks for YouTube Recommendations](https://dl.acm.org/ft_gateway.cfm?id=2959190&type=pdf). Paul Covington, Jay Adams, Emre Sargin. RecSys 2016. Google.

* [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://dl.acm.org/ft_gateway.cfm?id=3018665&type=pdf). Lei Zheng, Vahid Noroozi, Philip S. Yu. WSDM 2017. UIUC.