Bayesian Probabilistic Multi-Topic Matrix Factorization for Rating Prediction

Recently, Local Matrix Factorization (LMF) [Lee et al., 2013] has been shown to be more effective than traditional matrix factorization for rating prediction. The core idea for LMF is to first partition the original matrix into several smaller submatrices, further exploit local structures of submatrices for better low-rank approximation. Various clustering-based methods with heuristic extensions have been proposed for LMF in the literature. To develop a more principled solution for LMF, this paper presents a Bayesian Probabilistic Multi-Topic Matrix Factorization model. We treat the set of the rated items by a user as a document, and employ latent topic models to cluster items as topics. Subsequently, a user has a distribution over the set of topics. We further set topic-specific latent vectors for both users and items. The final prediction is obtained by an ensemble of the results from the corresponding topic-specific latent vectors in each topic. Using a multi-topic latent representation, our model is more powerful to reflect the complex characteristics for users and items in rating prediction, and enhance the model interpretability. Extensive experiments on large real-world datasets demonstrate the effectiveness of the proposed model.