Abstract
An important goal of online news system is to provide accurate personalized recommendation for users from mass news. One of the main problems in news recommendation is to obtain accurate news representation. Generally speaking, news is full of intellectual entities and common sense. However, existing news recommendation tends to ignore external knowledge and fail to fully detect potential knowledge links between news. In addition, news recommendation also faces the problem of user interest diversity. To solve the above problems, this paper proposes a knowledge enhancement and attention network based bnews recommendation model (KEAN) to enhance news title representation and user interest. The proposed model is mainly processed from two perspectives. First, for news titles, the entity embedding is enriched by aggregating information from its neighborhood in the knowledge graph, so as to obtain richer contextual embedding. Secondly, in order to meet the different interests in users, attention network is used to dynamically aggregate the information which is related to the current candidate news in user history. KEAN model can get rich feature representation by dealing with the correlation between entities and users’ interests. The experimental results show that the proposed model performs better than the other compared models with F1 and AUC being increased by 0.8%–1.2%, 0.6%–1.8% in Bing News and MIND dataset.
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Wang, Y., Gao, Q., Fan, J. (2022). KEAN: Knowledge-Enhanced and Attention Network for News Recommendation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_34
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DOI: https://doi.org/10.1007/978-3-031-10983-6_34
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