[1]周 帅,都云程,张仰森.个性化新闻推荐研究进展[J].计算机技术与发展,2023,33(02):1-8.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 001]
　ZHOU Shuai,DU Yun-cheng,ZHANG Yang-sen.Research Progress of Personalized News Recommendation[J].,2023,33(02):1-8.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 001]
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个性化新闻推荐研究进展()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
1-8
栏目:
综述
出版日期:
2023-02-10

文章信息/Info

Title:
Research Progress of Personalized News Recommendation
文章编号:
1673-629X(2023)02-0001-08
作者:
周 帅1 ; 都云程1 ; 张仰森2
1. 北京信息科技大学 计算机学院,北京 100101;
2. 北京信息科技大学 智能信息处理研究所,北京 100101
Author(s):
ZHOU Shuai1 ; DU Yun-cheng1 ; ZHANG Yang-sen2
1. School of Computer Science,Beijing Information Science and Technology University,Beijing 100101,China;
2. Institute of Intelligent Information Processing,Beijing Information Science and Technology University,Beijing 100101,China
关键词:
新闻推荐; 新闻建模; 用户兴趣建模; 个性化; 深度学习
Keywords:
news recommendation; news modeling; user interest modeling; personalization; deep learning
分类号:
TP309
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 001
摘要:
在线新闻推荐系统中,个性化新闻推荐是解决新闻信息爆炸问题,帮助用户便捷找到感兴趣新闻的一项重要技术。 在过去的研究中,各种新闻推荐技术层出不穷,在提高用户体验上已取得显著成效。 但由于在线新闻的多样性、动态性和时效性,新闻推荐仍是亟待发展且具有挑战性的。 首先,从传统方法和深度学习方法的角度,对个性化新闻推荐进行展开,并对新闻建模及用户兴趣建模进行归纳总结,分析两类方法的技术特点和不足之处。 然后,介绍个性化新闻推荐的数据集和评价方法,对典型新闻和用户兴趣建模方法的效果进行对比。 最后,对该领域未来可能的研究方向进行展望。希望能对个性化新闻推荐、自然语言处理和数据挖掘相关领域的研究有所帮助。
Abstract:
Personalized news recommendation is an important technology to solve the explosion of news information in the online newsrecommendation system and help users find interesting news conveniently. In the past research, various news recommendationtechnologies emerged one after another,and achieved remarkable results in improving user experience. But news recommendation is still achallenging problem due to the dynamic and timeliness of online news. Firstly, we develop personalized news recommendation fromtraditional methods and deep learning methods, summarize the news modeling and user interest modeling,and analyze their technicalcharacteristics and shortcomings. Then, we introduce the datasets and evaluation method of personalized news recommendation andcompare the effect of typical news and user interest modeling method. Finally,some research directions in this field are proposed. Wehope that it will be helpful to the research of personalized news recommendation, natural language processing and data mining.
更新日期/Last Update: 2023-02-10