Improving the accuracy of latent-space-based recommender systems by introducing a cut-off criterion

Recommender systems filter the items a user did not evaluate, in order to acquire knowledge on the those that might be suggested to her. To accomplish this objective, they employ the preferences the user expressed in forms of explicit ratings or of implicitly values collected through the browsing of the items. However, users have different rating behaviors (e.g., users might use just the ends of the rating scale, to expressed whether they loved or hated an item), while the system assumes that the users employ the whole scale. Over the last few years, {\em Singular Value Decomposition} ($SVD$) became the most popular and accurate form of recommendation, because of its capability of working with sparse data, exploiting latent features. This paper presents an approach that pre-filters the items a user evaluated and removes those she did not like. In other words, by analyzing a user's rating behavior and the rating scale she used, we capture and employ in the recommendation process only the items she really liked. Experimental results show that our form of filtering leads to more accurate recommendations.