Bayesian probabilistic tensor factorization for recommendation and rating aggregation with multicriteria evaluation data

Highlights：

• To the best of our knowledge, our work is the first attempt to apply Bayesian probabilistic tensor factorization to multicriteria recommendation. Our model, which we call “Bayesian probabilistic tensor factorization for multicriteria (BPTF-MC),” predicts the overall rating and the rating from each viewpoint simultaneously. It does this by using multicriteria latent features as additional factors.

• The BPTF-MC model enables the prediction of ratings for items by each user and of aggregated ratings from the evaluations of a small number of users.

• Experimental results for the Rakuten public datasets show that the BPTF-MC model achieves better performance than single-criterion models and low-rank tensor factorization models for both recommendation and rating aggregation.