Aspect-based latent factor model by integrating ratings and reviews for recommender system

In this work, we aim to propose a novel model, called Aspect-based Latent Factor Model (ALFM) to integrate ratings and review texts via latent factor model, in which by integrating rating matrix, user-review matrix and item-attribute matrix, the user latent factors and item latent factors with word latent factors can be derived. Our proposed model aggregates all review texts of the same user on the respective items and builds a user-review matrix by word frequencies. Similarly, an item's review is considered as all review texts of the same item collected from respective users. According to different information abstracted from review texts, we introduce two different kinds of item-attribute matrix to integrate the item-word frequencies and polarity scores of corresponding words. Experimental results on real-world data sets from amazon.com illustrate that our model can not only perform better than traditional models and art-of-state models on rating prediction task, but also accomplish cross-domain task through transferring word embedding.