Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks

Abstract

Learning users' preferences is critical to enable personalized recommendation services in various online applications such as e-commerce, entertainment and many others. In this paper, we study on how to learn users' preferences from abundant online activities, e.g., browsing and examination, which are usually called implicit feedbacks since they cannot be interpreted as users' likes or dislikes on the corresponding products directly. Pairwise preference learning algorithms are the state-of-the-art methods for this important problem, but they have two major limitations of low accuracy and low efficiency caused by noise in observed feedbacks and non-optimal learning steps in update rules. As a response, we propose a novel adaptive pairwise preference learning algorithm, which addresses the above two limitations in a single algorithm with a concise and general learning scheme. Specifically, in the proposed learning scheme, we design an adaptive utility function and an adaptive learning step for the aforementioned two problems, respectively. Empirical studies show that our algorithm achieves significantly better results than the state-of-the-art method on two real-world data sets.