  Although the latent factor model achieves good accuracy in rating prediction,
it suffers from many problems including cold-start, non-transparency, and
suboptimal results for individual user-item pairs. In this paper, we exploit
textual reviews and item images together with ratings to tackle these
limitations. Specifically, we first apply a proposed multi-modal aspect-aware
topic model (MATM) on text reviews and item images to model users' preferences
and items' features from different aspects, and also estimate the aspect
importance of a user towards an item. Then the aspect importance is integrated
into a novel aspect-aware latent factor model (ALFM), which learns user's and
item's latent factors based on ratings. In particular, ALFM introduces a weight
matrix 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, our
model could alleviate the data sparsity problem and gain good interpretability
for recommendation. Besides, every aspect rating is weighted by its aspect
importance, which is dependent on the targeted user's preferences and the
targeted item's features. Therefore, it is expected that the proposed method
can model a user's preferences on an item more accurately for each user-item
pair. Comprehensive experimental studies have been conducted on the Yelp 2017
Challenge dataset and Amazon product datasets to demonstrate the effectiveness
of our method.
