DEAMER: A Deep Exposure-Aware Multimodal Content-Based Recommendation System

Abstract

Modern content-based recommendation systems have greatly benefited from deep neural networks, which can effectively learn feature representations from item descriptions and user profiles. However, the supervision signals to guide the representation learning are generally incomplete (i.e., the majority of ratings are missing) and/or implicit (i.e., only historical interactions showing implicit preferences are available). The learned representations will be biased in this case; and consequently, the recommendations are over-specified. To alleviate this problem, we present a Deep Exposure-Aware Multimodal contEnt-based Recommender (i.e., DEAMER) in this paper. DEAMER can jointly exploit rating and interaction signals via multi-task learning. DEAMER mimics the expose-evaluate process in recommender systems where an item is evaluated only if it is exposed to the user. DEAMER generates the exposure status by matching multi-modal user and item content features. Then the rating value is predicted based on the exposure status. To verify the effectiveness of DEAMER, we conduct comprehensive experiments on a variety of e-commerce data sets. We show that DEAMER outperforms state-of-the-art shallow and deep recommendation models on recommendation tasks such as rating prediction and top-k recommendation. Furthermore, DEAMER can be adapted to extract insightful patterns of both users and items.