Joint Deep Recommendation Model Exploiting Reviews and Metadata Information

User-generated product reviews contain a lot of valuable information including users’ opinions on products and product features that is not fully exploited by the current recommendation models. Similarly, the metadata information related to the products, about the reviews and about the users who have written the reviews has rarely been exploited for recommender systems. These heterogeneous information sources have the potential to alleviate the cold start and sparsity problems and improve the quality of recommendations. In this paper, we present a joint deep recommendation model (JDRM) that consists of two parallel neural networks, learning lower-order feature interactions of users and items separately and higher-order feature interactions jointly using a shared last layer. Each of the networks is further composed of two sub-networks. One of the sub-networks focus on exploiting product reviews (of user/item) and the other sub-network learns user preferences/items properties leveraging metadata information along with the ratings. The learned latent features in each network are concatenated, thus producing the user and item latent feature vectors. We combine the two networks by introducing a shared layer on the top, which is a dense fully connected layer used to learn higher level latent features obtained from the two networks and produces final ratings. Extensive experiments on real-world datasets demonstrate that JDRM significantly outperforms state of the art recommendation models.