Similarity-Based Matrix Factorization for Item Cold-Start in Recommender Systems

In recommender systems (RS) one of the most used approaches is collaborative filtering (CF), which recommends items according to the behavior of similar users. Among CF approaches, those based on matrix factorization are generally more effective because they allow the system to discover the underlying characteristics of interactions between users and items. However, this approach presents the cold-start problem, which occurs because of the system's inability to recommend new items and/or accurately predict new users' preferences. This paper proposes a novel matrix factorization approach, which incorporates similarity of items using their metadata, in order to improve the rating prediction task in an item cold-start scenario. For this purpose, we explore semantic descriptions of items which are gathered from knowledge bases available online. Our approach is evaluated in two different and publicly available datasets and compared against content-based and collaborative algorithms. The experiments show the effectiveness of our approach in the item cold-start scenario.