News Recommendation System Based on Topic Embedding and Knowledge Embedding

作     者：ZHANG Haojie SUN Hui QI Baiwen SHEN Zhidong ZHANG Haojie;SUN Hui;QI Baiwen;SHEN Zhidong

作者机构：Key Laboratory of Aerospace Information Security and Trusted ComputingMinistry of Education/School of Cyber Science and EngineeringWuhan UniversityWuhan 430079HubeiChina Zhongnan HospitalWuhan UniversityWuhan 430072HubeiChina Engineering Research Center of CyberspaceYunnan UniversityKunming 650504YunnanChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2023年第28卷第1期

页      面：29-34页

核心收录：

学科分类：08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the Key Research&Development Projects in Hubei Province(2022BAA041 and 2021BCA124) the Open Foundation of Engineering Research Center of Cyberspace(KJAQ202112002)。 

主　　题：news recommendation knowledge embedding topic embedding historical behavior 

摘      要：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.