825 research outputs found
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
Although latent factor models (e.g., matrix factorization) achieve good
accuracy in rating prediction, they suffer from several problems including
cold-start, non-transparency, and suboptimal recommendation for local users or
items. In this paper, we employ textual review information with ratings to
tackle these limitations. Firstly, we apply a proposed aspect-aware topic model
(ATM) on the review text to model user preferences and item features from
different aspects, and estimate the aspect importance of a user towards an
item. The aspect importance is then 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 weighted matrix to associate those
latent factors with the same set of aspects discovered by ATM, 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, an aspect rating is weighted by an aspect importance,
which is dependent on the targeted user's preferences and 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 locally.
Comprehensive experimental studies have been conducted on 19 datasets from
Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves
significant improvement compared with strong baseline methods, especially for
users with only few ratings. Moreover, our model could interpret the
recommendation results in depth.Comment: This paper has been accepted by the WWW 2018 Conferenc
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