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Accuracy improvements for multi-criteria recommender systems

Recommender systems (RS) have shown to be valuable tools on e-commerce sites which help the customers identify the most relevant items within large product catalogs. In systems that rely on collaborative filtering, the generation of the product recommendations is based on ratings provided by the user community. While in many domains users are only allowed to attach an overall rating to the items, increasingly more online platforms allow their customers to evaluate the available items along different dimensions. Previous work has shown that these criteria ratings contain valuable information that can be exploited in the recommendation process.
 In this work we present new methods to leverage information derived from multi-dimensional ratings to improve the predictive accuracy of such multi-criteria recommender systems. In particular, we propose to use Support Vector regression to determine the relative importance of the individual criteria ratings and suggest to combine user- and item-based regression models in a weighted approach. Beside the automatic adjustment and optimization of the combination weights, we also explore different feature selection strategies to further improve the quality of the recommendations.
 An experimental analysis on two real-world rating datasets reveals that our method outperforms both recent single-rating algorithms based on matrix factorization as well as previous methods based on multi-criteria ratings in terms of the predictive accuracy.
 We therefore see the usage of multi-criteria customer ratings as a promising opportunity for e-commerce sites to improve the quality and precision of their online recommendation services.