2 research outputs found
Research Progress of News Recommendation Methods
Due to researchers'aim to study personalized recommendations for different
business fields, the summary of recommendation methods in specific fields is of
practical significance. News recommendation systems were the earliest research
field regarding recommendation systems, and were also the earliest
recommendation field to apply the collaborative filtering method. In addition,
news is real-time and rich in content, which makes news recommendation methods
more challenging than in other fields. Thus, this paper summarizes the research
progress regarding news recommendation methods. From 2018 to 2020, developed
news recommendation methods were mainly deep learning-based, attention-based,
and knowledge graphs-based. As of 2020, there are many news recommendation
methods that combine attention mechanisms and knowledge graphs. However, these
methods were all developed based on basic methods (the collaborative filtering
method, the content-based recommendation method, and a mixed recommendation
method combining the two). In order to allow researchers to have a detailed
understanding of the development process of news recommendation methods, the
news recommendation methods surveyed in this paper, which cover nearly 10
years, are divided into three categories according to the abovementioned basic
methods. Firstly, the paper introduces the basic ideas of each category of
methods and then summarizes the recommendation methods that are combined with
other methods based on each category of methods and according to the time
sequence of research results. Finally, this paper also summarizes the
challenges confronting news recommendation systems
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