News recommender systems (NRS) have been widely applied for online news
websites to help users find relevant articles based on their interests. Recent
methods have demonstrated considerable success in terms of recommendation
performance. However, the lack of explanation for these recommendations can
lead to mistrust among users and lack of acceptance of recommendations. To
address this issue, we propose a new explainable news model to construct a
topic-aware explainable recommendation approach that can both accurately
identify relevant articles and explain why they have been recommended, using
information from associated topics. Additionally, our model incorporates two
coherence metrics applied to assess topic quality, providing measure of the
interpretability of these explanations. The results of our experiments on the
MIND dataset indicate that the proposed explainable NRS outperforms several
other baseline systems, while it is also capable of producing interpretable
topics compared to those generated by a classical LDA topic model. Furthermore,
we present a case study through a real-world example showcasing the usefulness
of our NRS for generating explanations.Comment: 20 pages, submitted to a journa