Deep Learning Based Personalized Recommendation with Multi-View Information Integration.

With the rapid proliferation of images on e-commerce platforms today, embracing and integrating versatile information sources have become increasingly important in recommender systems. Owing to the heterogeneity in information sources and consumers, it is necessary and meaningful to consider the potential synergy between visual and textual content as well as consumers' different cognitive styles. This paper proposes a multi-view model, namely, Deep Multi-view Information iNtEgration (Deep-MINE), to take multiple sources of content (i.e., product images, descriptions and review texts) into account and design an end-to-end recommendation model. In doing so, stacked auto-encoder networks are deployed to map multi-view information into a unified latent space, a cognition layer is added to depict consumers' heterogeneous cognition styles and an integration module is introduced to reflect the interaction of multi-view latent representations. Extensive experiments on real world data demonstrate that Deep-MINE achieves high accuracy in product ranking, especially in the cold-start case. In addition, Deep-MINE is able to boost overall model performance compared with models taking a single view, further verifying the proposed model's effectiveness on information integration.