Adversarial Preference Learning with Pairwise Comparisons

When facing rich multimedia content and making a decision, users tend to be overwhelmed with redundant options. Recommendation system can improve the users' experience by predicting the possible preference of a given user. The vast majority of the literature adopts the collaborative framework, which relies on a static and fixed formulation of the rating score prediction function (in most cases an inner product function). However, such a static learning paradigm is not consistent with the dynamic feature of human intelligence. Motivated by this, we present a novel adversarial framework for collaborative ranking. On one hand, we leverage a deep generator to approximate an arbitrary continuous score function in terms of pairwise comparison. On the other hand, a discriminator provides personalized supervision signals with increasing difficulty. Different from the traditional static learning framework, our proposed approach enjoys a dynamic nature and unifies both the generative and the discriminative model for collaborative ranking. Comprehensive empirical studies on three real-world datasets show significant improvements of the adversarial framework over the state-of-the-art methods.