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Accurately modeling of news and users is the key to news recommendation. Researchers usually use auxiliary information such as social networks or item attributes to learn about news and user representation. However, existing recommendation systems neglect to explore the rich topics in the news. This paper considered the knowledge graph as the source of side information. Meanwhile, we used user topic preferences to improve recommendation performance. We proposed a new framework called NRTEH that was based on topic and entity preferences in user historical behavior. The core of our approach was the news encoder and the user encoder. Two encoders in NRTEH handled news titles from two perspectives to obtain news and user representation embedding: (1) extracting explicit and latent topic features from news and mining user preferences for them; and (2) extracting entities and propagating users\u2019 potential preferences in the knowledge graph. Experiments on a real-world dataset validated the effectiveness and efficiency of our approach.<\/jats:p>","DOI":"10.3390\/info14020060","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T03:28:38Z","timestamp":1674012518000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["News Recommendation Based on User Topic and Entity Preferences in Historical Behavior"],"prefix":"10.3390","volume":"14","author":[{"given":"Haojie","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4880-381X","authenticated-orcid":false,"given":"Zhidong","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Engineering Research Center of Cyberspace, Yunnan University, Kunming 650091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"ref_1","unstructured":"An, M., Wu, F., Wu, C., Zhang, K., Liu, Z., and Xie, X. 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