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Although the latent factor model achieves good accuracy in rating prediction,it suffers from many problems including cold-start, non-transparency, andsuboptimal results for individual user-item pairs. In this paper, we exploittextual reviews and item images together with ratings to tackle theselimitations. Specifically, we first apply a proposed multi-modal aspect-awaretopic model (MATM) on text reviews and item images to model users' preferencesand items' features from different aspects, and also estimate the aspectimportance of a user towards an item. Then the aspect importance is integratedinto a novel aspect-aware latent factor model (ALFM), which learns user's anditem's latent factors based on ratings. In particular, ALFM introduces a weightmatrix to associate those latent factors with the same set of aspects in MATM,such that the latent factors could be used to estimate aspect ratings. Finally,the overall rating is computed via a linear combination of the aspect ratings,which are weighted by the corresponding aspect importance. To this end, ourmodel could alleviate the data sparsity problem and gain good interpretabilityfor recommendation. Besides, every aspect rating is weighted by its aspectimportance, which is dependent on the targeted user's preferences and thetargeted item's features. Therefore, it is expected that the proposed methodcan model a user's preferences on an item more accurately for each user-itempair. Comprehensive experimental studies have been conducted on the Yelp 2017Challenge dataset and Amazon product datasets to demonstrate the effectivenessof our method.

MMALFM: Explainable Recommendation by Leveraging Reviews and Images