{"type":"ScholarlyArticle","title":"Sequential and Diverse Recommendation with Long Tail","authors":[{"name":"Yejin Kim","slug":"yejin-kim","given":"Yejin","family":"Kim","affiliation":"University of Texas Health Science Center at Houston"},{"name":"Kwangseob Kim","slug":"kwangseob-kim","given":"Kwangseob","family":"Kim","affiliation":"Kakao Corp."},{"name":"Chanyoung Park","slug":"chanyoung-park","given":"Chanyoung","family":"Park","affiliation":"Pohang University of Science and Technology"},{"name":"Hwanjo Yu","slug":"hwanjo-yu","given":"Hwanjo","family":"Yu","affiliation":"Pohang University of Science and Technology"}],"abstract":"Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction.","doi":"10.24963/ijcai.2019/380","url":"https://ijcai.org/proceedings/2019/380","pdf":"https://ijcai.org/proceedings/2019/0380.pdf","markdown":"https://ijcai.org/proceedings/2019/380.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/380","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 A-L","track":"Main track","number":380,"pages":{"first":2740,"last":2746},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Kwon, 2011] Gediminas Adomavicius and YoungOk Kwon. Maximizing aggregate recommen- dation diversity: A graph-theoretic approach. In (DiveRS 2011) RecSys, pages 3-10, 2011.","title":"Maximizing aggregate recommendation diversity: A graph-theoretic approach","authors":"Kwon Adomavicius, Gediminas Adomavicius, Youngok Kwon","year":"2011","venue":"DiveRS 2011) RecSys","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Adomavicius and Kwon, 2012] Gediminas Adomavicius and YoungOk Kwon. Improving aggregate recommen- dation diversity using ranking-based techniques. IEEE TKDE, 24(5):896-911, 2012.","title":"Gediminas Adomavicius and YoungOk Kwon. Improving aggregate recommendation diversity using ranking-based techniques","authors":"Kwon Adomavicius","year":"2012","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Anderson, 2004] Chris Anderson. The long tail. Wired mag- azine, 12(10):170-177, 2004.","title":"The long tail","authors":"Chris Anderson","year":"2004","venue":"Wired magazine","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Antikacioglu and Ravi, 2017] Arda Antikacioglu and R Ravi. Post processing recommender systems for diversity. In KDD, pages 707-716. ACM, 2017.","title":"Arda Antikacioglu and R Ravi. Post processing recommender systems for diversity","authors":"Ravi Antikacioglu","year":"2017","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bradley et al., 2000] PS Bradley, KP Bennett, and Ayhan Demiriz. Constrained k-means clustering. Microsoft Re- search, Redmond, pages 1-8, 2000.","title":"Constrained k-means clustering","authors":"Bradley","year":"2000","venue":"Microsoft Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheng et al., 2017] Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. Learning to recom- mend accurate and diverse items. In WWW, pages 183- 192. ACM, 2017.","title":"Learning to recommend accurate and diverse items","authors":"Cheng","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. On the proper- ties of neural machine translation: Encoder-decoder ap- proaches. arXiv preprint arXiv:1409.1259, 2014.","title":"On the properties of neural machine translation: Encoder-decoder approaches","authors":"Cho","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Christoffel et al., 2015] Fabian Christoffel, Bibek Paudel, Chris Newell, and Abraham Bernstein. Blockbusters and wallflowers: Accurate, diverse, and scalable recommen- dations with random walks. In RecSys, pages 163-170. ACM, 2015.","title":"Blockbusters and wallflowers: Accurate, diverse, and scalable recommendations with random walks","authors":"Fabian Christoffel, Bibek Paudel, Chris Newell, Abraham Bernstein","year":"2015","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hidasi et al., 2016] Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. Paral- lel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys, pages 241- 248. ACM, 2016.","title":"Parallel recurrent neural network architectures for feature-rich session-based recommendations","authors":"Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk","year":"2016","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed rep- resentations of words and phrases and their composition- ality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Oh et al., 2011] Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, and Seung-Taek Park. Novel recommendation based on personal popularity tendency. In ICDM, pages 507-516. IEEE, 2011.","title":"Novel recommendation based on personal popularity tendency","authors":"Oh","year":"2011","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Park and Tuzhilin, 2008] Yoon-Joo Park and Alexander Tuzhilin. The long tail of recommender systems and how to leverage it. In RecSys, pages 11-18. ACM, 2008.","title":"The long tail of recommender systems and how to leverage it","authors":"Yoon-Joo Park, Alexander Tuzhilin","year":"2008","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"Yoon-Joo Park. The adaptive clustering method for the long tail problem of recommender systems. IEEE TKDE, 25(8):1904-1915, 2013.","title":"The adaptive clustering method for the long tail problem of recommender systems","authors":"Yoon-Joo Park","year":"2013","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pages 452-461. AUAI Press, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rendle et al., 2010] Steffen Rendle, Christoph Freuden- thaler, and Lars Schmidt-Thieme. Factorizing personal- ized markov chains for next-basket recommendation. In WWW, pages 811-820. ACM, 2010.","title":"Factorizing personalized markov chains for next-basket recommendation","authors":"Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Sculley, 2010] David Sculley. Web-scale k-means cluster- ing. In WWW, pages 1177-1178. ACM, 2010.","title":"Web-scale k-means clustering","authors":"David Sculley","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Vargas and Castells, 2011] Saúl Vargas and Pablo Castells. Rank and relevance in novelty and diversity metrics for recommender systems. In RecSys, pages 109-116. ACM, 2011.","title":"Saúl Vargas and Pablo Castells. Rank and relevance in novelty and diversity metrics for recommender systems","authors":"Castells Vargas","year":"2011","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2015] Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. Learn- ing hierarchical representation model for nextbasket rec- ommendation. In SIGIR, pages 403-412. ACM, 2015.","title":"Learning hierarchical representation model for nextbasket recommendation","authors":"Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, Xueqi Cheng","year":"2015","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Xia et al., 2008] Fen Xia, Tie-Yan Liu, Jue Wang, Wen- sheng Zhang, and Hang Li. Listwise approach to learning to rank: theory and algorithm. In ICML, pages 1192-1199. ACM, 2008.","title":"Listwise approach to learning to rank: theory and algorithm","authors":"Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Hang Li","year":"2008","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yin et al., 2012] Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. Challenging the long tail recommenda- tion. VLDB, 5(9):896-907, 2012.","title":"Challenging the long tail recommendation","authors":"Bin Hongzhi Yin, Jing Cui, Junjie Li, Chen Yao, Chen","year":"2012","venue":"VLDB","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yu et al., 2016] Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. A dynamic recurrent model for next basket recommendation. In SIGIR, pages 729-732. ACM, 2016.","title":"A dynamic recurrent model for next basket recommendation","authors":"Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan","year":"2016","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ziegler et al., 2005] Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. Improving recom- mendation lists through topic diversification. In WWW, pages 22-32. ACM, 2005.","title":"Improving recommendation lists through topic diversification","authors":"Cai-Nicolas Ziegler, Sean M Mcnee, Joseph A Konstan, Georg Lausen","year":"2005","venue":"WWW","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}