Topic-Centric Explanations for News Recommendation

Author:

Liu Dairui1ORCID,Greene Derek1ORCID,Li Irene2ORCID,Jiang Xuefei1ORCID,Dong Ruihai3ORCID

Affiliation:

1. School of Computer Science, University College Dublin, Dublin, Ireland

2. Information Technology Center, The University of Tokyo, Tokyo Japan

3. School of Computer Science, University College Dublin, Dublin Ireland

Abstract

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 a 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 measured by coherence metrics. Furthermore, we present a case study through real-world examples showcasing the usefulness of our NRS for generating explanations.

Publisher

Association for Computing Machinery (ACM)

Reference58 articles.

1. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

2. Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. 2019. Neural News Recommendation with Long- and Short-term User Representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 336–345.

3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR.

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