A Deep Hybrid Model for Recommendation by jointly leveraging ratings, reviews and metadata information. (January 2021) - Khan, Zahid Younas; Niu, Zhendong; Nyamawe, Ally S.; Haq, Ijaz ul
Abstract: Although matrix factorization (MF) based collaborative filtering (CF) and deep learning approaches have achieved great success, there is still much room for improvement in recommender systems. Most of the existing approaches mainly adopt product ratings, reviews or content features in order to predict unknown rating for a user–item pair. In the discourse matter, some recent works attempted to obtain better latent representations of users and items by integrating different multi-source data, however, the heterogeneity of data is still a problem deserving study. Such models usually face two issues: (1) They extract the representations in a static and independent manner, thus ignoring the correlations between latent features learned from different information sources. (2) There is no unified framework that can mutually learn latent features from different sources such as ratings, reviews and meta-data of users, items and reviews. In the proposed model, called A Deep Hybrid Model for Recommendation (DHMR), we propose a joint deep model for learning higher-order non-linear latent feature interactions from reviews and metadata information. Further, we incorporate user–item interactions (from user–item ratings matrix) adopting MF model into the neural network. Thus, the proposed model consists of two parallel neural networks and an MF based model that are integrated by the attention and MLP layers at the top, learning lower-order (linear and non-linear) feature interactions of users and items separately and higher-order non-linear feature interactions jointly. Extensive experiments on real-world datasets demonstrate that DHMR significantly outperforms state-of-the-art recommendation models. Highlights: A hybrid neural model for joint deep learning of ratings, review text and metadata. Learning from integrated features complements for more insights of the preferences. The features obtained in this way give better user/item representation. Experimental results validates the claim and outperforms the baseline alternatives.