Bayesian Personalized Ranking with Multi-Channel User Feedback

In many domains, users provide unary feedback via a number of different ‘channels’. In this paper, we propose an approach called Multi-Feedback Bayesian Personalized Ranking (MF-BPR). The innovation of MFBRP is a sampling method designed to simultaneously exploit unary feedback from multiple channels during training. The key to our approach is to map different feedback channels to different ‘levels’ that reflect the contribution that each type of feedback can have in the training phase.

Our MF-BPR can be considered hybrid, since it simultaneously uses different sources of feedback uses multiple feedback channels simultaneously.

BPR-MF is based on the insight that user feedback collected via various channels reflects different strengths of user preference, and the sampling method of BPR can exploit these differences. In MF-BPR we introduce a non-uniform sampler that takes into account the level (importance) of the feedback channel. We propose a non-uniform distribution for p(L) where the cardinality of feedback as well as the importance of a level is taken into account with a weight factor. In our experiments, we found that the inverse rank of positive levels are good candidates for weights.
The MF-BPR is evaluated on three datasets and its performance is compared with different methods using 4-fold cross validation. The ground truth and relevant recommendations however, are different in the three datasets depending on the problem.

Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. 2016. Bayesian Personalized Ranking with Multi-Channel User Feedback. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 361-364.