The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic View

Matrix factorization is a well-known and effective methodology for top-k list recommendation. It became widely known during the Netflix challenge in 2006, and since then, many adapted and improved versions have been published. A particularly interesting matrix factorization algorithm called iALS (for implicit Alternating Least Squares) adapts the method for implicit feedback, i.e. a setting where only a very small amount of positive labels are available along with a majority of unknown labels. Compared to the classical task of rating prediction, learning from implicit feedback is applicable to many more domains, as the data is more abundant and requires less effort to elicit from users. However, the sparsity, imbalance, and implicit nature of the signal also pose unique challenges to retrieving the most relevant items to recommend.