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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
会议名称The 37th International Conference on Information Systems, ICIS, 2016
会议录名称The 37th International Conference on Information Systems
会议日期2016-12-11
会议地点Dublin, Ireland
摘要

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.

关键词Recommender Systems Data Sparsity Collaborative Filtering Hidden Markov Model
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13027
专题综合信息系统研究中心
通讯作者Zhang, Haidong
作者单位1.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
推荐引用方式
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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