Unbiased Pairwise Learning from Biased Implicit Feedback

Abstract

Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback, and we cannot know whether the non-interacted feedback is positive or negative. Furthermore, positive feedback for rare items is observed less frequently than popular items. The relevance of such rare items is often underestimated. Existing solutions to such challenges are subject to bias toward the ideal loss function of interest or accept a simple pointwise approach, which is inappropriate for a ranking task. In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking metrics. Subsequently, we propose a theoretically grounded unbiased estimator for this ideal pairwise loss and a corresponding algorithm, Unbiased Bayesian Personalized Ranking. A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner. Through theoretical analysis, we provide the critical statistical properties of the proposed unbiased estimator and a practical variance reduction technique. Empirical evaluations using real-world datasets demonstrate the practical strength of our approach.