A survey on knowledge-aware news recommender systems

News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behaviour. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. A new classification method divides the models into four categories: frameworks based on the vector space model, on semantic similarities, on distance, and on knowledge graph embeddings. Moreover, the underlying recommendation algorithms, as well as their evaluations are analysed. ... mehrLastly, open issues in the domain of knowledge-aware news recommendation are identified and potential research directions proposed.