  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 as well as
related fields in natural language processing and data mining.
