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Modeling Idle Customers to Tackle the Sparsity Problem in Time-dependent Recommendation
Zhang, Haidong; Ni, Wancheng; Li, Xin; Yang, Yiping
2016
Conference NameThe 37th International Conference on Information Systems, ICIS, 2016
Source PublicationThe 37th International Conference on Information Systems
Conference Date2016-12-11
Conference PlaceDublin, Ireland
Abstract

Recommender systems have been widely used to provide personal and convenient services for users. As one of successful recommendation methods, collaborative filtering explores users’ interests from item consumptions. However, it suffers from the data sparsity problem that most users have interacted with a small number of items. Particularly, data sparsity causes the discontinuous user activities over time, which limits the time-dependent recommendation methods for tracking users’ changing interests. In this paper, we extend existing methods and propose an inhibited hidden Markov model to alleviate the sparsity problem. The model considers the statuses of users’ interests at each time unit and allows for capturing users’ dynamic interests under idle status. We derive an EM algorithm to estimate the model parameters and predict users’ actions. We perform a comprehensive experiment on the datasets of various sparsity levels. The results show our model has been consistently and significantly  better than the state-of-the-art algorithms.

KeywordRecommender Systems Data Sparsity Collaborative Filtering Hidden Markov Model
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13027
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
4.Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Zhang, Haidong,Ni, Wancheng,Li, Xin,et al. Modeling Idle Customers to Tackle the Sparsity Problem in Time-dependent Recommendation[C],2016.
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