  Nowadays, more and more news readers tend to read news online where they have
access to millions of news articles from multiple sources. In order to help
users to find the right and relevant content, news recommender systems (NRS)
are developed to relieve the information overload problem and suggest news
items that users might be interested in. In this paper, we highlight the major
challenges faced by the news recommendation domain and identify the possible
solutions from the state-of-the-art. Due to the rapid growth of building
recommender systems using deep learning models, we divide our discussion in two
parts. In the first part, we present an overview of the conventional
recommendation solutions, datasets, evaluation criteria beyond accuracy and
recommendation platforms being used in NRS. In the second part, we explain the
deep learning-based recommendation solutions applied in NRS. Different from
previous surveys, we also study the effects of news recommendations on user
behavior and try to suggest the possible remedies to mitigate these effects. By
providing the state-of-the-art knowledge, this survey can help researchers and
practical professionals in their understanding of developments in news
recommendation algorithms. It also sheds light on potential new directions
