{"type":"ScholarlyArticle","title":"What to Do Next: Modeling User Behaviors by Time-LSTM","authors":[{"name":"Yu Zhu","slug":"yu-zhu","given":"Yu","family":"Zhu","affiliation":"† State Key Lab of CAD&CG , College of Computer Science , Zhejiang University , China"},{"name":"Hao Li","slug":"hao-li","given":"Hao","family":"Li","affiliation":"College of Information and Technology , Northwest University of China"},{"name":"Yikang Liao","slug":"yikang-liao","given":"Yikang","family":"Liao","affiliation":"† State Key Lab of CAD&CG , College of Computer Science , Zhejiang University , China"},{"name":"Beidou Wang","slug":"beidou-wang","given":"Beidou","family":"Wang","affiliation":"‡ College of Computer Science , Zhejiang University , China; School of Computing Science , Simon Fraser University , Canada"},{"name":"Ziyu Guan","slug":"ziyu-guan","given":"Ziyu","family":"Guan","affiliation":"College of Information and Technology , Northwest University of China"},{"name":"Haifeng Liu","slug":"haifeng-liu","given":"Haifeng","family":"Liu","affiliation":"College of Information and Technology , Northwest University of China"},{"name":"Deng Cai","slug":"deng-cai","given":"Deng","family":"Cai","affiliation":"† State Key Lab of CAD&CG , College of Computer Science , Zhejiang University , China"}],"abstract":"Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.","doi":"10.24963/ijcai.2017/504","url":"https://ijcai.org/proceedings/2017/504","pdf":"https://ijcai.org/proceedings/2017/0504.pdf","markdown":"https://ijcai.org/proceedings/2017/504.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/504","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":504,"pages":{"first":3602,"last":3608},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aghabozorgi and Wah, 2009] Saeed R Aghabozorgi and Teh Ying Wah. 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