Generating Knowledge-Based Attentive User Representations for Sparse Interaction Recommendation

Deep neural networks (DNNs) have been widely imported into collaborative-filtering (CF) based recommender systems and yielded remarkable superiority over traditional recommendation models. However, most deep CF-based models perform weakly when observed user-item interactions are sparse since user preferences and item characteristics are inferred mainly based on observed (historical) interactions. To address this problem, we propose a deep knowledge-enhanced recommendation model in this paper. Specifically, to augment user/item representations in the scenario of sparse historical user-item interactions, we first incorporate the knowledge from open knowledge graphs and personal information of users as side information, from which sufficient features of users and items are extracted. Second, to well capture shifted user preferences, we leverage a memory component constituted by recently interacted items rather than all historical ones. Third, attentive user representations are generated by attention mechanism to capture the diversity of user preferences. Furthermore, we build a convolutional neural network to pool the latent features in user representations for better user modeling, which enhances recommendation performance further. Our extensive experiments conducted against two real-world datasets, i.e., Douban movie and NetEase music, demonstrate our model's remarkable superiority over the state-of-the-art deep recommendation models.