{"type":"ScholarlyArticle","title":"User Modeling with Click Preference and Reading Satisfaction for News Recommendation","authors":[{"name":"Chuhan Wu","slug":"chuhan-wu","given":"Chuhan","family":"Wu","affiliation":"Department of Electronic Engineering & BNRist , Tsinghua University , Beijing 100084 , China"},{"name":"Fangzhao Wu","slug":"fangzhao-wu","given":"Fangzhao","family":"Wu","affiliation":"Microsoft Research Asia , Beijing 100080 , China"},{"name":"Tao Qi","slug":"tao-qi","given":"Tao","family":"Qi","affiliation":"Department of Electronic Engineering & BNRist , Tsinghua University , Beijing 100084 , China"},{"name":"Yongfeng Huang","slug":"yongfeng-huang","given":"Yongfeng","family":"Huang","affiliation":"Department of Electronic Engineering & BNRist , Tsinghua University , Beijing 100084 , China"}],"abstract":"Modeling user interest is critical for accurate news recommendation. Existing news recommendation methods usually infer user interest from click behaviors on news. However, users may click a news article because attracted by its title shown on the news website homepage, but may not be satisfied with its content after reading. In many cases users close the news page quickly after click. In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation. More specifically, we propose a personalized reading speed metric to measure users’ satisfaction with news content. We learn embeddings of users from the news content they have read and their satisfaction with these news to model their interest in news content. In addition, we also learn another user embedding from the news titles they have clicked to model their preference in news titles. We combine both kinds of user embeddings into a unified user representation for news recommendation. We train the user representation model using two supervised learning tasks built from user behaviors, i.e., news title based click prediction and news content based satisfaction prediction, to encourage our model to recommend the news articles which not only are likely to be clicked but also have the content satisfied by the user. Experiments on real-world dataset show our method can effectively boost the performance of user modeling for news recommendation.","doi":"10.24963/ijcai.2020/418","url":"https://ijcai.org/proceedings/2020/418","pdf":"https://ijcai.org/proceedings/2020/0418.pdf","markdown":"https://ijcai.org/proceedings/2020/418.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/418","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":418,"pages":{"first":3023,"last":3029},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24455/file_24455.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[An et al., 2019] Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. Neural news recom- mendation with long-and short-term user representations. In ACL, pages 336-345, 2019.","title":"Neural news recommendation with long-and short-term user representations","authors":"An","year":"2019","venue":"ACL","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":"[Ge et al., 2020] Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. Graph enhanced representation learning for news recommendation. In WWW, pages 2863- 2869, 2020.","title":"Graph enhanced representation learning for news recommendation","authors":"Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":4,"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, 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":5,"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, 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":6,"raw":"[Kim et al., 2014] Youngho Kim, Ahmed Hassan, Ryen W White, and Imed Zitouni. Modeling dwell time to predict click-level satisfaction. In WSDM, pages 193-202, 2014.","title":"Modeling dwell time to predict click-level satisfaction","authors":"Kim","year":"2014","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2010] Lihong Li, Wei Chu, John Langford, and Robert E Schapire. A contextual-bandit approach to per- sonalized news article recommendation. In WWW, pages 661-670, 2010.","title":"A contextual-bandit approach to personalized news article recommendation","authors":"Lihong Li, Wei Chu, John Langford, Robert E Schapire","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li et al., 2011] Lei Li, Dingding Wang, Tao Li, Daniel Knox, and Balaji Padmanabhan. Scene: a scalable two- stage personalized news recommendation system. In SI- GIR, pages 125-134, 2011.","title":"Scene: a scalable twostage personalized news recommendation system","authors":"Lei Li, Dingding Wang, Tao Li, Daniel Knox, Balaji Padmanabhan","year":"2011","venue":"SI-GIR","doi":null,"url":null,"internal_url":null},{"position":10,"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":11,"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, 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":12,"raw":"[Liu et al., 2016] Yiqun Liu, Xiaohui Xie, Chao Wang, Jian- Yun Nie, Min Zhang, and Shaoping Ma. Time-aware click model. TOIS, 35(3):1-24, 2016.","title":"Time-aware click model","authors":"Yiqun Liu, Xiaohui Xie, Chao Wang, Jian-Yun Nie, Min Zhang, Shaoping Ma","year":"2016","venue":"TOIS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lu et al., 2018] Hongyu Lu, Min Zhang, and Shaoping Ma. Between clicks and satisfaction: Study on multi-phase user preferences and satisfaction for online news reading. In SIGIR, pages 435-444, 2018.","title":"Between clicks and satisfaction: Study on multi-phase user preferences and satisfaction for online news reading","authors":"Hongyu Lu, Min Zhang, Shaoping Ma","year":"2018","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lu et al., 2019] Hongyu Lu, Min Zhang, Weizhi Ma, Ce Wang, Feng xia, Yiqun Liu, Leyu Lin, and Shaoping Ma. Effects of user negative experience in mobile news streaming. In SIGIR, pages 705-714, 2019.","title":"Effects of user negative experience in mobile news streaming","authors":"Hongyu Lu, Min Zhang, Weizhi Ma, Ce Wang, Feng Xia, Yiqun Liu, Leyu Lin, Shaoping Ma","year":"2019","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mehrotra et al., 2017] Rishabh Mehrotra, Imed Zitouni, Ahmed Hassan Awadallah, Ahmed El Kholy, and Madian Khabsa. User interaction sequences for search satisfaction prediction. In SIGIR, pages 165-174, 2017.","title":"User interaction sequences for search satisfaction prediction","authors":"Rishabh Mehrotra, Imed Zitouni, Ahmed Hassan Awadallah, Ahmed El Kholy, Madian Khabsa","year":"2017","venue":"SIGIR","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, 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":"[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":19,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NIPS, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":20,"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":21,"raw":"[Wu et al., 2019a] Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. Neural news recommendation with attentive multi-view learning. In IJCAI, pages 3863-3869, 2019.","title":"Neural news recommendation with attentive multi-view learning","authors":"Wu","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/536"},{"position":22,"raw":"[Wu et al., 2019b] Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. Npa: Neural news recommendation with personalized at- tention. In KDD, pages 2576-2584, 2019.","title":"Npa: Neural news recommendation with personalized attention","authors":"Wu","year":"2019","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2019c] Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. Neural news rec- ommendation with multi-head self-attention. In EMNLP- IJCNLP, pages 6390-6395, 2019.","title":"Neural news recommendation with multi-head self-attention","authors":"Wu","year":"2019","venue":"EMNLP-IJCNLP","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wu et al., 2019d] 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, 2019.","title":"Neural demographic prediction using search query","authors":"Wu","year":"2019","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yi et al., 2014] Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. Beyond clicks: dwell time for personalization. In RecSys, pages 113-120, 2014.","title":"Beyond clicks: dwell time for personalization","authors":"Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan, Nan Liu, Suju Rajan","year":"2014","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhao et al., 2019] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. Recommending what video to watch next: a mul- titask ranking system. In RecSys, pages 43-51, 2019.","title":"Recommending what video to watch next: a multitask ranking system","authors":"Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi","year":"2019","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhou et al., 2018] Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, and Wenwu Ou. Jump: a joint predictor for user click and dwell time. In IJCAI, pages 3704-3710, 2018.","title":"Jump: a joint predictor for user click and dwell time","authors":"Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Chengwei Wang, Shichen Liu, Wenwu Ou","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhu et al., 2019] Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, and Li Guo. Dan: Deep attention neural network for news recommendation. In AAAI, vol- ume 33, pages 5973-5980, 2019.","title":"Dan: Deep attention neural network for news recommendation","authors":"Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, Li Guo","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}