A survey on news recommender system Dealing with timeliness, dynamic user interest and content quality, and effects of recommendation on news readers
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 in terested in. In this paper, we highlight the major challenges faced by the news recommen dation 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 dis cussion in two parts. In the first part, we present an overview of the conventional recom mendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in NRS. In the second part, we explain deep learning based recom mendation 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 reme dies 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.
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