Bayesian Personalized Ranking with Multi-Channel User Feedback

Abstract

Pairwise learning-to-rank algorithms have been shown to allow recommendersystems to leverage unary user feedback. We proposeMulti-feedback Bayesian Personalized Ranking (MF-BPR), a pairwisemethod that exploits different types of feedback with an extendedsampling method. The feedback types are drawn from different“channels”, in which users interact with items (e.g., clicks,likes, listens, follows, and purchases). We build on the insight thatdifferent kinds of feedback, e.g., a click versus a like, reflect differentlevels of commitment or preference. Our approach differs fromprevious work in that it exploits multiple sources of feedback simultaneouslyduring the training process. The novelty of MF-BPRis an extended sampling method that equates feedback sources with“levels” that reflect the expected contribution of the signal. Wedemonstrate the effectiveness of our approach with a series of experimentscarried out on three datasets containing multiple typesof feedback. Our experimental results demonstrate that with a rightsampling method, MF-BPR outperforms BPR in terms of accuracy.We find that the advantage of MF-BPR lies in its ability to leveragelevel information when sampling negative items.