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.
CITATION STYLE
Shen, C., Yang, D., & Xiao, Y. (2019). A Deep Recommendation Model Incorporating Adaptive Knowledge-Based Representations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 481–486). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_71
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