个性化新闻推荐方法研究综述

doi:10.3778/j.issn.1673-9418.2303026

摘要/Abstract

摘要： 个性化新闻推荐是帮助用户获取其感兴趣的新闻信息和缓解信息过载的重要技术。近年来，随着信息技术和社会发展，个性化新闻推荐得到了日益广泛的研究，并在改善用户的新闻阅读体验方面取得了显著成功。对基于深度学习的个性化新闻推荐方法进行了系统性综述。首先，分类介绍了个性化新闻推荐方法并分析各自特点及影响因素；然后，给出了个性化新闻推荐的总体框架，并对基于深度学习的个性化新闻推荐方法进行了分析总结；在此基础上，重点综述了基于图结构学习的个性化新闻推荐方法，包括基于用户-新闻交互图、知识图谱和社交关系图的新闻推荐；最后，分析了当前个性化新闻推荐所面临的挑战，探讨了如何解决个性化新闻推荐系统中数据稀疏性、模型可解释性、推荐结果多样性和新闻隐私保护等问题，并在未来研究方向中展望了更具体可操作的研究思路和方法。

关键词: 个性化新闻推荐, 深度学习, 图结构学习

Abstract: Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload. In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in improving the news reading experience of users. This paper aims to systematically summarize personalized news recommendation methods based on deep learning. Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors. Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized. On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph. Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction.

Key words: personalized news recommendation, deep learning, graph structure learning

孟祥福, 霍红锦, 张霄雁, 王琬淳, 朱金侠. 个性化新闻推荐方法研究综述[J]. 计算机科学与探索, 2023, 17(12): 2840-2860.

MENG Xiangfu, HUO Hongjin, ZHANG Xiaoyan, WANG Wanchun, ZHU Jinxia. Survey of Research on Personalized News Recommendation Approaches[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(12): 2840-2860.

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