A cross-modal warm-up solution for the cold-start problem in collaborative filtering recommender systems

Abstract

We present a cross-modal recommendation engine that leverages multiple domains of data while performing matrix factorization. We show how our approach has the potential to alleviate the cold-start problem for new items, one of the notorious limitations of Collaborative Filtering (CF) techniques.