A multi-criteria item-based collaborative filtering framework

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.