A Categorized Item Recommender System Coping with User Interest Changes
Su Sande Ko Ko, Rachsuda Jiamthapthaksin · International Journal of Machine Learning and Computing · 2014
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.