{"type":"ScholarlyArticle","title":"PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training","authors":[{"name":"Wei Zhao","slug":"wei-zhao","given":"Wei","family":"Zhao","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences; Tencent Inc."},{"name":"Benyou Wang","slug":"benyou-wang","given":"Benyou","family":"Wang","affiliation":"Tencent Inc."},{"name":"Jianbo Ye","slug":"jianbo-ye","given":"Jianbo","family":"Ye","affiliation":"Pennsylvania State University"},{"name":"Yongqiang Gao","slug":"yongqiang-gao","given":"Yongqiang","family":"Gao","affiliation":"Tencent Inc."},{"name":"Min Yang","slug":"min-yang","given":"Min","family":"Yang","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"},{"name":"Xiaojun Chen","slug":"xiaojun-chen","given":"Xiaojun","family":"Chen","affiliation":"Shenzhen University"}],"abstract":"Recommender systems provide users with ranked lists of items based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of items' attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short-Term Information in top-n reCommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used real-world datasets.","doi":"10.24963/ijcai.2018/511","url":"https://ijcai.org/proceedings/2018/511","pdf":"https://ijcai.org/proceedings/2018/0511.pdf","markdown":"https://ijcai.org/proceedings/2018/511.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/511","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":511,"pages":{"first":3676,"last":3682},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"Maska X","title":null,"authors":"X Maska","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Saves Family X [4] Hours X","title":"Hours X","authors":null,"year":null,"venue":"Saves Family X","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Secret!","title":"Secret!","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"Yankees","title":null,"authors":"Yankees","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"for Me Last Round","title":"Me Last Round","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"Vie de Chateau [1Down","title":"Vie de Chateau","authors":null,"year":null,"venue":"Vie de Chateau","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"X Mask X Hours","title":null,"authors":"X Mask, Hours","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"Saves Family X [6] Crocodile Dundee 2","title":"Saves Family X","authors":null,"year":null,"venue":"Crocodile Dundee","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"Princess Bride X Expendable Yankees","title":"Expendable Yankees","authors":"Princess Bride, X","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Princess Bride X","title":null,"authors":"Princess Bride, X","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Saves Family X [4Last Valley X","title":"Saves Family X [4Last Valley X","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"Mask X Session 9","title":"Mask X Session 9","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"Yankees Hours X","title":null,"authors":"Yankees Hours, X","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"Valentin X 9 Souls Princess Bride Chong's Up [2] Wax Mask","title":"X 9 Souls Princess Bride Chong's Up [2","authors":"Valentin","year":null,"venue":"Wax Mask","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"Damn Yankees","title":null,"authors":"Damn Yankees","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"Secret! Cody Banks 2 ] Hours","title":"Secret! Cody Banks","authors":null,"year":null,"venue":"Secret! Cody Banks","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"Saves Family [9X","title":"Saves Family","authors":null,"year":null,"venue":"Saves Family","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"Beatles Dundee 2","title":"Beatles Dundee","authors":null,"year":null,"venue":"Beatles Dundee","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"X Down Mask Hours","title":"X Down Mask Hours","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"Saves Family [7] Session 9","title":"Saves Family","authors":null,"year":null,"venue":"Saves Family","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"Princess Bride X","title":null,"authors":"Princess Bride, X","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"X Bride X Last Valley Saves Family [5Mask","title":"X Bride X Last Valley Saves Family [5Mask","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"Tales Session 9 Crocodile Dundee 2","title":"Tales Session 9 Crocodile Dundee","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"Damn Yankees","title":null,"authors":"Damn Yankees","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"Cheech Chong's Up References [Bobadilla et al., 2013] Jesus Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutierrez. Recom- mender systems survey. Knowledge-based systems, 46:109-132, 2013.","title":"Jesus Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutierrez. Recommender systems survey","authors":"Cheech Chong, ; Bobadilla","year":"2013","venue":"Knowledge-based systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Chen et al., 2017] Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu, and Min Sun. Show, adapt and tell: Adversarial train- ing of cross-domain image captioner. arXiv preprint arXiv:1705.00930, 2017.","title":"Show, adapt and tell: Adversarial training of cross-domain image captioner","authors":"Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu, Min Sun","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In NIPS, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[He et al., 2016] Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. Fast matrix factorization for on- line recommendation with implicit feedback. In SIGIR, pages 549-558. ACM, 2016.","title":"Fast matrix factorization for online recommendation with implicit feedback","authors":"He","year":"2016","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Hernando et al., 2016] Antonio Hernando, Jesús Bobadilla, and Fernando Ortega. A non negative matrix factoriza- tion for collaborative filtering recommender systems based on a bayesian probabilistic model. Knowledge-Based Sys- tems, 97:188-202, 2016.","title":"A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model","authors":"Hernando","year":"2016","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. In ICLR, 2015.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recom- mender systems. Computer, 42(8), 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Koren, 2010] Yehuda Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4):89-97, 2010.","title":"Collaborative filtering with temporal dynamics","authors":"Koren ;, Yehuda Koren","year":"2010","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Liu and Yang, 2008] Nathan N Liu and Qiang Yang. Eigen- rank: a ranking-oriented approach to collaborative filter- ing. In SIGIR, pages 83-90. ACM, 2008.","title":"Eigenrank: a ranking-oriented approach to collaborative filtering","authors":"N Nathan, Qiang Liu, Yang","year":"2008","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Liu et al., 2018] Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, and Hongyan Li. Generative adversarial network for abstractive text summarization. In AAAI, 2018.","title":"Generative adversarial network for abstractive text summarization","authors":"Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Lu et al., 2015] Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, and Guangquan Zhang. Recommender sys- tem application developments: a survey. Decision Support Systems, 74:12-32, 2015.","title":"Recommender system application developments: a survey","authors":"Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang, Guangquan Zhang","year":"2015","venue":"Decision Support Systems","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[McNee et al., 2006] Sean M McNee, John Riedl, and Joseph A Konstan. Being accurate is not enough: how ac- curacy metrics have hurt recommender systems. In CHI, pages 1097-1101. ACM, 2006.","title":"Being accurate is not enough: how accuracy metrics have hurt recommender systems","authors":"John Sean M Mcnee, Joseph A Riedl, Konstan","year":"2006","venue":"CHI","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Mirza and Osindero, 2014] Mehdi Mirza and Simon Osin- dero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.","title":"Conditional generative adversarial nets","authors":"Mehdi Mirza, Simon Osindero","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Mnih and Salakhutdinov, 2008] Andriy Mnih and Ruslan R Salakhutdinov. Probabilistic matrix factorization. In NIPS, pages 1257-1264, 2008.","title":"Andriy Mnih and Ruslan R Salakhutdinov","authors":"Salakhutdinov Mnih","year":"2008","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Qiang et al., 2013] Runwei Qiang, Feng Liang, and Jianwu Yang. Exploiting ranking factorization machines for mi- croblog retrieval. In CIKM, pages 1783-1788. ACM, 2013.","title":"Exploiting ranking factorization machines for microblog retrieval","authors":"Qiang","year":"2013","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Reed et al., 2016] Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. Generative adversarial text to image synthesis. In ICML, pages 1060-1069. JMLR.org, 2016.","title":"Generative adversarial text to image synthesis","authors":"Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee","year":"2016","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452-461, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Rennie and Srebro, 2005] Jasson DM Rennie and Nathan Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, pages 713-719. ACM, 2005.","title":"Fast maximum margin matrix factorization for collaborative prediction","authors":"Rennie, Srebro ; ] Jasson, D M Rennie, Nathan Srebro","year":"2005","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Srebro et al., 2005] Nathan Srebro, Jason Rennie, and Tommi S Jaakkola. Maximum-margin matrix factoriza- tion. In NIPS, pages 1329-1336, 2005.","title":"Maximum-margin matrix factorization","authors":"Nathan Srebro, Jason Rennie, Tommi S Jaakkola","year":"2005","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Sutton et al., 2000] Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. Policy gradient methods for reinforcement learning with function approx- imation. In NIPS, pages 1057-1063, 2000.","title":"Policy gradient methods for reinforcement learning with function approximation","authors":"David A Richard S Sutton, Mcallester, P Satinder, Yishay Singh, Mansour","year":"2000","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Tu et al., 2015] Wenting Tu, David W Cheung, Nikos Mamoulis, Min Yang, and Ziyu Lu. Activity-partner rec- ommendation. In PAKDD, pages 591-604, 2015.","title":"Activity-partner recommendation","authors":"Wenting Tu, David W Cheung, Nikos Mamoulis, Min Yang, Ziyu Lu","year":"2015","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Wang et al., 2017] Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. Irgan: A minimax game for unifying gener- ative and discriminative information retrieval models. In SIGIR, pages 515-524, 2017.","title":"Irgan: A minimax game for unifying generative and discriminative information retrieval models","authors":"Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, Dell Zhang","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wu et al., 2016a] Caihua Wu, Junwei Wang, Juntao Liu, and Wenyu Liu. Recurrent neural network based recom- mendation for time heterogeneous feedback. Knowledge- Based Systems, 109:90-103, 2016.","title":"Recurrent neural network based recommendation for time heterogeneous feedback","authors":"Wu","year":"2016","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wu et al., 2016b] Chao-Yuan Wu, Amr Ahmed, Alex Beu- tel, and Alexander J Smola. Joint training of ratings and re- views with recurrent recommender networks. ICLR, 2016.","title":"Joint training of ratings and reviews with recurrent recommender networks","authors":"Wu","year":"2016","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wu et al., 2017] Chao-Yuan Wu, Amr Ahmed, Alex Beu- tel, Alexander J Smola, and How Jing. Recurrent recom- mender networks. In WSDM, pages 495-503. ACM, 2017.","title":"Recurrent recommender networks","authors":"Chao-Yuan, Amr Wu, Alex Ahmed, Alexander J Beutel, How Smola, Jing","year":"2017","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Yu et al., 2017] Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. In AAAI, pages 2852-2858, 2017.","title":"Seqgan: Sequence generative adversarial nets with policy gradient","authors":"Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Yuan et al., 2016] Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang. Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In CIKM, pages 227- 236. ACM, 2016.","title":"Lambdafm: learning optimal ranking with factorization machines using lambda surrogates","authors":"Yuan","year":"2016","venue":"CIKM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}