Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines

Abstract

User interactions can be considered to constitute different feedback channels, for example, view, click, like or follow, that provide implicit information on users’ preferences. Each implicit feedback channel typically carries a unary, positive-only signal that can be exploited by collaborative filtering models to generate lists of personalized recommendations. This article investigates how a learning-to-rank recommender system can best take advantage of implicit feedback signals from multiple channels. We focus on Factorization Machines (FMs) with Bayesian Personalized Ranking (BPR), a pairwise learning-to-rank method, that allows us to experiment with different forms of exploitation. We perform extensive experiments on three datasets with multiple types of feedback to arrive at a series of insights. We compare conventional, direct integration of feedback types with our proposed method, which exploits multiple feedback channels during the sampling process of training. We refer to our method as multi-channel sampling. Our results show that multi-channel sampling outperforms conventional integration, and that sampling with the relative “level” of feedback is always superior to a level-blind sampling approach. We evaluate our method experimentally on three datasets in different domains and observe that with our multi-channel sampler the accuracy of recommendations can be improved considerably compared to the state-of-the-art models. Further experiments reveal that the appropriate sampling method depends on particular properties of datasets such as popularity skewness.