News Recommendation System Based on Topic Embedding and Knowledge Embedding

ZHANG Haojie ¹SUN Hui ²QI Baiwen ²SHEN Zhidong³

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作者信息

1. Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education/School of Cyber Science and Engineering,Wuhan University,Wuhan 430079,Hubei,China
2. Zhongnan Hospital,Wuhan University,Wuhan 430072,Hubei,China
3. Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education/School of Cyber Science and Engineering,Wuhan University,Wuhan 430079,Hubei,China;Engineering Research Center of Cyberspace,Yunnan University,Kunming 650504,Yunnan,China
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Abstract

News recommendation system is designed to deal with massive news and provide personalized recommendations for users.Ac-curately 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 us-ers 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.

Key words

news recommendation/knowledge embedding/topic embedding/historical behavior

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基金项目

Key Research&Development Projects in Hubei Province(2022BAA041)

Key Research&Development Projects in Hubei Province(2021BCA124)

Open Foundation of Engineering Research Center of Cyberspace(KJAQ202112002)

出版年

2023

武汉大学自然科学学报(英文版)

武汉大学

武汉大学自然科学学报(英文版)

CSTPCDCSCD北大核心

影响因子：0.066

ISSN：1007-1202

参考文献量12

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