Article,
A survey on knowledge-aware news recommender systems
Semantic Web Journal, (2022)
Abstract
News consumption has shifted over time from traditional media to online platforms, which use recommendation al-
gorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their
preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have
proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming out-
dated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) 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 cap-
turing 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. We propose a taxon-
omy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based
methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in
the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.
