Criteria Chains: A Novel Multi-Criteria Recommendation Approach

Abstract

Recommender systems (RSs) have been successfully applied to alleviate the problem of information overload and assist users' decision makings. Multi-criteria recommender systems is one of the RSs which utilizes users' multiple ratings on different aspects of the items (i.e., multi-criteria ratings) to predict user preferences. Traditional approaches usually predict ratings on each criterion individually and aggregate them together to estimate the user preferences. In this paper, we propose an approach named as "Criteria Chains", where each combination of the criteria can be utilized in a way of contextual situations in order to better predict the multi-criteria ratings. Our experimental results based on the TripAdvisor and YahooMovies rating data sets demonstrate that our proposed approach is able to improve the performance of multi-criteria item recommendations.