Research Information System
11th International Joint Conference on Computer Science and Software Engineering (JCSSE), Pattaya, Thailand, 14 - 16 May 2014, pp.18-22
Collaborative filtering methods are utilized to provide personalized recommendations for users in order to alleviate information overload problem in different domains. Traditional collaborative filtering methods operate on a user-item matrix in which each user reveal her admiration about an item based on a single criterion. However, recent studies indicate that recommender systems depending on multi-criteria can improve accuracy level of referrals. Since multi-criteria rating-based collaborative filtering systems consider users in multi-aspects of items, they are more successful at forming correlation-based user neighborhoods. Although, proposed multi-criteria user-based collaborative filtering algorithms' accuracy results are very promising, they have online scalability issues. In this paper, we propose an item-based multi-criteria collaborative filtering framework. In order to determine appropriate neighbor selection method, we compare traditional correlation approaches with multi-dimensional distance metrics. Also, we investigate accuracy performance of statistical regression-based predictions. According to real data-based experiments, it is possible to produce more accurate recommendations by utilizing multi-criteria item-based collaborative filtering algorithm instead of a single criterion rating-based algorithm.