A Deep Recommendation Model Incorporating Adaptive Knowledge-Based Representations

Deep neural networks (DNNs) have been widely imported into collaborative filtering (CF) based recommender systems and yielded remarkable superiority, but most models perform weakly in the scenario of sparse user-item interactions. To address this problem, we propose a deep knowledge-based recommendation model in which item knowledge distilled from open knowledge graphs and user information are both incorporated to extract sufficient features. Moreover, our model compresses features by a convolutional neural network and adopts memory-enhanced attention mechanism to generate adaptive user representations based on latest interacted items rather than all historical records. Our extensive experiments conducted against a real-world dataset demonstrate our model's remarkable superiority over some state-of-the-art deep models.