Deep learning in news recommender systems: A comprehensive survey, challenges and future trends

Abstract

Nowadays, people prefer to read news articles from online sources worldwide due to their easiness and availability. For the last few years, online searching for required information or content has been replaced by item recommendation, and news recommendation is also not an exception. For news recommendations, News Recommender System (NRS) helps the users to find the appropriate and pertinent content, alleviate the problem of information overload, and propose news that would be of interest to news readers. NRS also assists different users all around the world in this regard by recommending the most recent news articles based on their interests and past preferences. Many techniques such as traditional, Deep Learning (DL), and hybrid have been proposed to solve the NRS challenges and issues. DL techniques are considered one of the best techniques and have been successfully applied in various fields such as Natural Language Processing (NLP) and Computer Vision (CV). This survey article provides a detailed analysis of DL models-based techniques to build NRS. In this regard, firstly, a comprehensive comparison is provided between published survey articles on NRS and this research work. Secondly, it discusses the background of recommendation systems and their techniques. Furthermore, NRS is explored along with its current research challenges. Then background knowledge of DL and its methods have been discussed along with the analysis of year-wise published relevant articles having DL as the applied technique. The survey also presents widely used datasets and performance evaluation metrics used in the relevant literature. Finally, a detailed discussion provides several future directions and open research challenges for the researchers to consider DL applications in NRS.