Selection of Negative Samples for One-class Matrix Factorization.

Many recommender systems have only implicit user feedback. The two possible ratings are positive and negative, but only part of positive entries are observed. One-class matrix factorization (MF) is a popular approach for such scenarios by treating some missing entries as negative. Two major ways to select negative entries are by sub-sampling a set with similar size to that of observed positive entries or by including all missing entries as negative. They are referred to as “subsampled” and “full” approaches in this work, respectively. Currently detailed comparisons between these two selection schemes on large-scale data are still lacking. One important reason is that the “full” approach leads to a hard optimization problem after treating all missing entries as negative. In this paper, we successfully develop efficient optimization techniques to solve this challenging problem so that the “full” approach becomes practically viable. We then compare in detail the two approaches “subsampled” and “full” for selecting negative entries. Results show that the “full” approach of including much more missing entries as negative yields better results.