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    <abstract>Explanations accompanying a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user’s confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user’s preferences, based on the user’s purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation metrics.</abstract>
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%0 Conference Proceedings
%T Knowledge-Grounded Natural Language Recommendation Explanation
%A Colas, Anthony
%A Araki, Jun
%A Zhou, Zhengyu
%A Wang, Bingqing
%A Feng, Zhe
%Y Belinkov, Yonatan
%Y Hao, Sophie
%Y Jumelet, Jaap
%Y Kim, Najoung
%Y McCarthy, Arya
%Y Mohebbi, Hosein
%S Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F colas-etal-2023-knowledge
%X Explanations accompanying a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user’s confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user’s preferences, based on the user’s purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation metrics.
%R 10.18653/v1/2023.blackboxnlp-1.1
%U https://aclanthology.org/2023.blackboxnlp-1.1
%U https://doi.org/10.18653/v1/2023.blackboxnlp-1.1
%P 1-15

Markdown (Informal)

[Knowledge-Grounded Natural Language Recommendation Explanation](https://aclanthology.org/2023.blackboxnlp-1.1) (Colas et al., BlackboxNLP-WS 2023)

• Knowledge-Grounded Natural Language Recommendation Explanation (Colas et al., BlackboxNLP-WS 2023)

ACL

• Anthony Colas, Jun Araki, Zhengyu Zhou, Bingqing Wang, and Zhe Feng. 2023. Knowledge-Grounded Natural Language Recommendation Explanation. In Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 1–15, Singapore. Association for Computational Linguistics.