BPMR: Bayesian Probabilistic Multivariate Ranking
This addresses the need for principled multi-aspect ranking in recommender systems to improve performance and explainability, representing an incremental advance over existing methods.
The paper tackled the problem of ranking items based on multiple user preference aspects in recommender systems, proposing a probabilistic multivariate tensor factorization framework that generalizes single-aspect ranking, with experiments on a large dataset confirming its effectiveness.
Multi-aspect user preferences are attracting wider attention in recommender systems, as they enable more detailed understanding of users' evaluations of items. Previous studies show that incorporating multi-aspect preferences can greatly improve the performance and explainability of recommendation. However, as recommendation is essentially a ranking problem, there is no principled solution for ranking multiple aspects collectively to enhance the recommendation. In this work, we derive a multi-aspect ranking criterion. To maintain the dependency among different aspects, we propose to use a vectorized representation of multi-aspect ratings and develop a probabilistic multivariate tensor factorization framework (PMTF). The framework naturally leads to a probabilistic multi-aspect ranking criterion, which generalizes the single-aspect ranking to a multivariate fashion. Experiment results on a large multi-aspect review rating dataset confirmed the effectiveness of our solution.