{"type":"ScholarlyArticle","title":"Neural News Recommendation with Attentive Multi-View Learning","authors":[{"name":"Chuhan Wu","slug":"chuhan-wu","given":"Chuhan","family":"Wu","affiliation":"Department of Electronic Engineering , Tsinghua University , Beijing 100084 , China"},{"name":"Fangzhao Wu","slug":"fangzhao-wu","given":"Fangzhao","family":"Wu","affiliation":"Microsoft Research Asia , Beijing 100080 , China"},{"name":"Mingxiao An","slug":"mingxiao-an","given":"Mingxiao","family":"An","affiliation":"University of Science and Technology of China , Hefei 230026 , China"},{"name":"Jianqiang Huang","slug":"jianqiang-huang","given":"Jianqiang","family":"Huang","affiliation":"Peking University , Beijing 100871 , China"},{"name":"Yongfeng Huang","slug":"yongfeng-huang","given":"Yongfeng","family":"Huang","affiliation":"Department of Electronic Engineering , Tsinghua University , Beijing 100084 , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research Asia , Beijing 100080 , China"}],"abstract":"Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.","doi":"10.24963/ijcai.2019/536","url":"https://ijcai.org/proceedings/2019/536","pdf":"https://ijcai.org/proceedings/2019/0536.pdf","markdown":"https://ijcai.org/proceedings/2019/536.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/536","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":536,"pages":{"first":3863,"last":3869},"keywords":["Natural Language Processing: Natural Language Processing","Machine Learning: Deep Learning","Machine Learning: Data Mining","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bansal et al., 2015] Trapit Bansal, Mrinal Das, and Chi- ranjib Bhattacharyya. Content driven user profiling for comment-worthy recommendations of news and blog ar- ticles. In RecSys., pages 195-202. ACM, 2015.","title":"Content driven user profiling for comment-worthy recommendations of news and blog articles","authors":"Trapit Bansal, Mrinal Das, Chiranjib Bhattacharyya","year":"2015","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheng et al., 2016] Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. Wide & deep learning for recommender systems. In DLRS, pages 7-10, 2016.","title":"Wide & deep learning for recommender systems","authors":"Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir","year":"2016","venue":"DLRS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Das et al., 2007] Abhinandan S Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, pages 271-280. ACM, 2007.","title":"Google news personalization: scalable online collaborative filtering","authors":"Mayur Abhinandan S Das, Ashutosh Datar, Shyam Garg, Rajaram","year":"2007","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[De Francisci Morales et al., 2012] Gianmarco De Fran- cisci Morales, Aristides Gionis, and Claudio Lucchese. From chatter to headlines: harnessing the real-time web for personalized news recommendation. In WSDM, pages 153-162. ACM, 2012.","title":"From chatter to headlines: harnessing the real-time web for personalized news recommendation","authors":"Francisci De, Morales","year":"2012","venue":"Gianmarco De Francisci Morales","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Glorot et al., 2011] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In AISTATS, pages 315-323, 2011.","title":"Deep sparse rectifier neural networks","authors":"Xavier Glorot, Antoine Bordes, Yoshua Bengio","year":"2011","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Guo et al., 2017] Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: a factorization-machine based neural network for ctr predic- tion. In AAAI, pages 1725-1731. AAAI Press, 2017.","title":"Deepfm: a factorization-machine based neural network for ctr prediction","authors":"Guo","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":"/proceedings/2017/239"},{"position":7,"raw":"[Huang et al., 2013] Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. Learning deep structured semantic models for web search using click- through data. In CIKM, pages 2333-2338. ACM, 2013.","title":"Learning deep structured semantic models for web search using clickthrough data","authors":"Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck","year":"2013","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[IJntema et al., 2010] Wouter IJntema, Frank Goossen, Flav- ius Frasincar, and Frederik Hogenboom. Ontology-based news recommendation. In Proceedings of the 2010 EDBT/ICDT Workshops, page 16. ACM, 2010.","title":"Ontology-based news recommendation","authors":"Ijntema","year":"2010","venue":"Proceedings of the 2010 EDBT/ICDT Workshops","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Khattar et al., 2018] Dhruv Khattar, Vaibhav Kumar, Va- sudeva Varma, and Manish Gupta. Weave& rec: A word embedding based 3-d convolutional network for news rec- ommendation. In CIKM, pages 1855-1858. ACM, 2018.","title":"Weave& rec: A word embedding based 3-d convolutional network for news recommendation","authors":"Dhruv Khattar, Vaibhav Kumar, Vasudeva Varma, Manish Gupta","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kim, 2014] Yoon Kim. Convolutional neural networks for sentence classification. In EMNLP, pages 1746-1751, 2014.","title":"Convolutional neural networks for sentence classification","authors":"Yoon Kim","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kumar et al., 2017] Vaibhav Kumar, Dhruv Khattar, Shashank Gupta, and Vasudeva Varma. Word semantics based 3-d convolutional neural networks for news recom- mendation. In 2017 IEEE International Conference on Data Mining Workshops, pages 761-764, 2017.","title":"Word semantics based 3-d convolutional neural networks for news recommendation","authors":"Vaibhav Kumar, Dhruv Khattar, Shashank Gupta, Vasudeva Varma","year":"2017","venue":"IEEE International Conference on Data Mining Workshops","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lavie et al., 2010] Talia Lavie, Michal Sela, Ilit Oppen- heim, Ohad Inbar, and Joachim Meyer. User attitudes to- wards news content personalization. International journal of human-computer studies, 68(8):483-495, 2010.","title":"User attitudes towards news content personalization","authors":"Talia Lavie, Michal Sela, Ilit Oppenheim, Ohad Inbar, Joachim Meyer","year":"2010","venue":"International journal of human-computer studies","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lian et al., 2018] Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. Towards better representation learning for personalized news recommendation: a multi- channel deep fusion approach. In IJCAI, pages 3805- 3811, 2018.","title":"Towards better representation learning for personalized news recommendation: a multichannel deep fusion approach","authors":"Lian","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2010] Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. Personalized news recommendation based on click behavior. In IUI, pages 31-40. ACM, 2010.","title":"Personalized news recommendation based on click behavior","authors":"Jiahui Liu, Peter Dolan, Elin Rønby Pedersen","year":"2010","venue":"IUI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Okura et al., 2017] Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. Embedding-based news recommendation for millions of users. In KDD, pages 1933-1942. ACM, 2017.","title":"Embedding-based news recommendation for millions of users","authors":"Okura","year":"2017","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In EMNLP, pages 1532-1543, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher Manning","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Phelan et al., 2011] Owen Phelan, Kevin McCarthy, Mike Bennett, and Barry Smyth. Terms of a feather: Content- based news recommendation and discovery using twitter. In ECIR, pages 448-459. Springer, 2011.","title":"Terms of a feather: Contentbased news recommendation and discovery using twitter","authors":"Owen Phelan, Kevin Mccarthy, Mike Bennett, Barry Smyth","year":"2011","venue":"ECIR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Rendle, 2012] Steffen Rendle. Factorization machines with libfm. TIST, 3(3):57, 2012.","title":"Factorization machines with libfm","authors":"Steffen Rendle","year":"2012","venue":"TIST","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Son et al., 2013] Jeong-Woo Son, A Kim, Seong-Bae Park, et al. A location-based news article recommendation with explicit localized semantic analysis. In SIGIR, pages 293- 302. ACM, 2013.","title":"A location-based news article recommendation with explicit localized semantic analysis","authors":"Jeong-Woo Son, Seong-Bae Kim, Park","year":"2013","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Srivastava et al., 2014] Nitish Srivastava, Geoffrey E Hin- ton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1):1929-1958, 2014.","title":"Dropout: a simple way to prevent neural networks from overfitting","authors":"Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2018] Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. Dkn: Deep knowledge-aware net- work for news recommendation. In WWW, pages 1835- 1844, 2018.","title":"Dkn: Deep knowledge-aware network for news recommendation","authors":"Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2019a] Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. Neural news recommendation with topic-aware news representa- tion. In ACL, 2019.","title":"Neural news recommendation with topic-aware news representation","authors":"Wu","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wu et al., 2019b] Chuhan Wu, Fangzhao Wu, Junxin Liu, Shaojian He, Yongfeng Huang, and Xing Xie. Neural de- mographic prediction using search query. In WSDM, pages 654-662. ACM, 2019.","title":"Neural demographic prediction using search query","authors":"Wu","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wu et al., 2019c] Chuhan Wu, Fangzhao Wu, Junxin Liu, and Yongfeng Huang. Npa: Neural news recommendation with personalized attention. In KDD. ACM, 2019.","title":"Npa: Neural news recommendation with personalized attention","authors":"Wu","year":"2019","venue":"KDD. ACM","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhai et al., 2016] Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, and Zhongfei Mark Zhang. Deepintent: Learning attentions for online advertising with recurrent neural net- works. In KDD, pages 1295-1304. ACM, 2016.","title":"Deepintent: Learning attentions for online advertising with recurrent neural networks","authors":"Zhai","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zheng et al., 2018] Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. Drn: A deep reinforcement learning frame- work for news recommendation. In WWW, pages 167-176, 2018.","title":"Drn: A deep reinforcement learning framework for news recommendation","authors":"Zheng","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}