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
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, firstname.lastname@example.org; 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.