A neural joint model for rating prediction recommendation

Reviews text reflects user interests and product characteristics, which provide rich useful semantic textual information for modeling user and product. Some existing works improve the performance of rating prediction by distinguishing the usefulness of each review. However, most of them ignore the fact that the usefulness of each review should be dynamic and dependent on the target user-product pair. For example, when we predict the user's ratings of a restaurant, the user's previous reviews about restaurants should be more useful. To be more specific, when we model the target user-product pair, the usefulness of each review is associated with the target user and item. To address the above issue, we use a Review-level Dynamic Topic Co-Attention, which combines all reviews written by the user and all reviews that were written for the product to assign attention score for each review collaboratively. Besides, we believe that product category and user co-purchase information can further improve the rating prediction performance. In this paper, we propose a neural joint model called NMRP based on reviews, products category, and users' co-purchase information for rating prediction recommendation. First, the Review Extraction Module learns user and product information from reviews. Then, HIN Extraction Module extracts the association feature for a given target user-product pair from a heterogeneous information network (HIN). Finally, the two parts of data are connected and input into the feature interaction method Attentional Factorization Machines (AFM), to achieve rating prediction. The experimental study on three datasets from Amazon shows that our model outperforms recently proposed baselines such as DeepCoNN, TransNets, and NARRE.