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Nowadays, more and more news readers tend to read news online where they haveaccess to millions of news articles from multiple sources. In order to helpusers to find the right and relevant content, news recommender systems (NRS)are developed to relieve the information overload problem and suggest newsitems that users might be interested in. In this paper, we highlight the majorchallenges faced by the news recommendation domain and identify the possiblesolutions from the state-of-the-art. Due to the rapid growth of buildingrecommender systems using deep learning models, we divide our discussion in twoparts. In the first part, we present an overview of the conventionalrecommendation solutions, datasets, evaluation criteria beyond accuracy andrecommendation platforms being used in NRS. In the second part, we explain thedeep learning-based recommendation solutions applied in NRS. Different fromprevious surveys, we also study the effects of news recommendations on userbehavior and try to suggest the possible remedies to mitigate these effects. Byproviding the state-of-the-art knowledge, this survey can help researchers andpractical professionals in their understanding of developments in newsrecommendation algorithms. It also sheds light on potential new directions

News Recommender System: A review of recent progress, challenges, and opportunities