个人新闻建议:调查 (Personalized News Recommendation: A Survey)

Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users' news reading experience. However, there are still many unsolved problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this paper 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 paper can provide up-to-date and comprehensive views to help readers understand the personalized news recommendation field. We hope this paper can facilitate research on personalized news recommendation and as well as related fields in natural language processing and data mining.

翻译：个人化新闻建议是帮助用户发现其感兴趣的新闻信息并减轻其信息过量的重要方法,经过数十年的广泛研究,在改善用户的新闻阅读经验方面取得了显著成功,然而,仍有许多尚未解决的问题和挑战需要进一步研究。为了帮助研究人员掌握个人化新闻建议方面过去几年来的进展,我们在本文件中全面概述个人化新闻建议。我们不但没有采用传统的新闻建议分类方法,而是在本文中提出一种新颖的观点,以了解个人化新闻建议基于其核心问题和相关技术与挑战而提出的个人化新闻建议。我们首先审查个人化新闻建议系统解决每个核心问题的技术及其面临的挑战。我们接下来,我们介绍个人化新闻建议的公共数据集和评价方法。然后我们讨论个人化新闻建议系统责任的要点。最后,我们提出一些值得今后调查的研究方向。本文可以提供最新和全面的观点,帮助读者了解个人化新闻建议领域。我们希望这份文件能够促进个人化新闻建议的研究,并作为自然语言领域的相关数据处理。