A review of challenges, algorithms and evaluation methods in news recommendation

Abstract

News reading is an important social activity and to help readers quickly find news articles of their interest, news content providers and aggregators use recommender systems. Such systems are designed to address a variety of challenges. Inspiration for algorithmic design is taken from various domains which has resulted in the creation of an enormous body of literature. Also, different methods are used for evaluation of the recommendation algorithms. In this study, we review these developments and present three major components in news recommendation research. First, we list and categorise the challenges faced while designing news recommender systems. We especially list the different algorithmic designs used for generating personalised and non-personalised recommendations. We discuss the major neural network architectures that are being increasingly used for both collaborative and content-based recommender systems. Next, we list the two major evaluation methods and also list some popular datasets used in evaluation. Finally, we identify the emerging trends in news recommender research. We find that the issues related to fake news, trust and use of personal data for news recommendation are gaining wider attention, and deep learning methods are being increasingly used to address these issues.