Abstract
News recommendation system is designed to deal with massive news and provide personalized recommendations for users. Accurately capturing user preferences and modeling news and users is the key to news recommendation. In this paper, we propose a new framework, news recommendation system based on topic embedding and knowledge embedding (NRTK). NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding :1) extracting explicit and latent topic features from news and mining users' preferences for them in historical behaviors; 2) extracting entities and propagating users' potential preferences in the knowledge graph. Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.
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