Personalized News Recommendation: Methods and Challenges
Personalized News Recommendation: Methods and Challenges
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DOI：
10.1145/3530257
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发表时间：
2023-01-01
影响因子：
5.6
通讯作者：
Xie，Xing
中科院分区：
文献类型：
--
作者：
Wu，Chuhan;Wu，Fangzhao;Xie，Xing
个性化新闻推荐对于用户发现感兴趣的新闻信息、缓解信息过载具有重要意义。虽然它已经被广泛研究了几十年，并在改善用户体验方面取得了显着的成功，但仍有许多问题和挑战需要进一步研究。为了帮助研究人员掌握个性化新闻推荐的研究进展，本文对个性化新闻推荐进行了全面的综述。不同于传统的新闻推荐方法分类，本文从个性化新闻推荐的核心问题、相关技术和挑战出发，提出了一种新的视角来理解个性化新闻推荐。我们首先回顾了个性化新闻推荐系统中解决每个核心问题的技术以及它们所面临的挑战。接下来，我们介绍了公共数据集和个性化新闻推荐的评估方法。然后，我们讨论了提高个性化新闻推荐系统的责任的关键点。最后，提出了几个值得进一步研究的方向。这篇文章可以提供最新的和全面的意见，个性化的新闻推荐。希望本文能对个性化新闻推荐以及自然语言处理和数据挖掘等相关领域的研究有所帮助。
Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are still many problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation, in this article, we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this article, we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This article can provide up-to-date and comprehensive views on personalized news recommendation. We hope this article can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining.