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Online news recommender systems aim to address the information explosion ofnews and make personalized recommendation for users. In general, news languageis highly condensed, full of knowledge entities and common sense. However,existing methods are unaware of such external knowledge and cannot fullydiscover latent knowledge-level connections among news. The recommended resultsfor a user are consequently limited to simple patterns and cannot be extendedreasonably. Moreover, news recommendation also faces the challenges of hightime-sensitivity of news and dynamic diversity of users' interests. To solvethe above problems, in this paper, we propose a deep knowledge-aware network(DKN) that incorporates knowledge graph representation into newsrecommendation. DKN is a content-based deep recommendation framework forclick-through rate prediction. The key component of DKN is a multi-channel andword-entity-aligned knowledge-aware convolutional neural network (KCNN) thatfuses semantic-level and knowledge-level representations of news. KCNN treatswords and entities as multiple channels, and explicitly keeps their alignmentrelationship during convolution. In addition, to address users' diverseinterests, we also design an attention module in DKN to dynamically aggregate auser's history with respect to current candidate news. Through extensiveexperiments on a real online news platform, we demonstrate that DKN achievessubstantial gains over state-of-the-art deep recommendation models. We alsovalidate the efficacy of the usage of knowledge in DKN.

DKN: Deep Knowledge-Aware Network for News Recommendation