Cold-start recommendations in Collective Matrix Factorization

This work aims to explore the quality of cold-start recommendations derived from collective matrix factorization models [11] in collaborative filtering with explicit-feedback data in the form of ratings. Recommender systems based on collaborative filtering are typically constructed solely based on data about useritem interactions [6], such as movies rated by different users, which result in domain-independent and easily-implementable models, but have the disadvantage of only being able to make recommendations about users and items for which there is interactions data available (known as warm-start recommendations in the literature). In many settings however, there is oftentimes additional side information available about users and/or items, which is not used in the most common models such as low-rank matrix factorization [6] or kNN-based formulas [10], but which can be used both to improve recommendation models that take interactions data, and to make recommendations in the absence of interactions data (so-called cold-start recommendations). This work focuses on the second case: studying recommendations from matrix factorization models that are based on attributes data without interactions data.