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A HIDDEN SEMI-MARKOV APPROACH FOR TIME-DEPENDENT RECOMMENDATION
Zhang, Haidong; Ni, Wancheng; Li, Xin; Yang, Yiping
2016
Conference NameThe 20th Pacific Asia Conference on Information Systems Proceedings, PACIS 2016 Proceedings
Source PublicationPacific Asia Conference on Information Systems 2016 Proceedings
Conference Date2016-6-27
Conference PlaceTaiwan, China
Abstract

Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users’ preferences often change over time, which leads to the studies on time-dependent recommender systems. However, most existing approaches to deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users’ interests. Particularly, this model allows for users to stay in different (latent) interest states for different time periods, which is beneficial to model the heterogeneous length of users’ interest and focuses. We derive an EM algorithm to estimate the parameter of the framework, and predict users’ actions. Experiments on a real-world dataset show that our model significantly outperforms the state-of-the-art benchmark methods. Further analyses show that the performance depends on the allowed heterogeneity of latent states and the existence of user interest heterogeneity in the dataset.

KeywordHidden Semi-markov Model Time Dependent Recommendation Collaborative Filtering Recommender System
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13026
Collection综合信息系统研究中心
Corresponding AuthorZhang, Haidong
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.Department of Information Systems, City University of Hong Kong, Hong Kong, China
4.Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Zhang, Haidong,Ni, Wancheng,Li, Xin,et al. A HIDDEN SEMI-MARKOV APPROACH FOR TIME-DEPENDENT RECOMMENDATION[C],2016.
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