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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 399-404 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.444

A Categorized Item Recommender System Coping with User Interest Changes

Su Sande Ko Ko and Rachsuda Jiamthapthaksin
Abstract—One significant characteristic of data in specific domain like movie challenges research in recommender systems that user preferences naturally changes over time. Traditional collaborative filtering (CF) method does not take in consideration sequences of customer’s rating, which reflects changes of customer’s preference over a period of time. This paper proposes a novel recommender system that overcomes the limitation of CF by combining collaborative filtering and sequential pattern mining with time interval which reflects user’s preference changes over a period of time. Sequential patterns of categories of items are generated which represents and summarizes interest changes of users varied over time, and are used for revising recommended items produced by traditional CF. Experimental results show that the proposed system show improvements over the traditional collaborative filtering method.

Index Terms—Collaborative filtering, interest drift, sequential pattern mining, recommender system.

The authors are with the Computer Science Department, Faculty of Science and Technology, Assumption University, Bangkok, Thailand (e-mail: susande86@ gmail.com, rachsuda@scitech.au.edu; tel.: +66 2719-1515 ext. 3681, 3682).


Cite: Su Sande Ko Ko and Rachsuda Jiamthapthaksin, "A Categorized Item Recommender System Coping with User Interest Changes," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 399-404, 2014.

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