Generate Natural Language Explanations for Recommendation

By Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang. 2021

70 citations recorded by the source corpus.

Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the effectiveness, transparency, persuasiveness and trustworthiness of recommender systems. Current explainable recommendation models mostly generate textual explanations based on pre-defined sentence templates. However, the expressiveness power of template-based explanation sentences are limited to the pre-defined expressions, and manually defining the expressions require significant human efforts. Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation. In particular, we propose a hierarchical sequence-to-sequence model (HSS) for personalized explanation generation. Different from conventional sentence generation in NLP research, a great challenge of explanation generation in e-commerce recommendation is that not all sentences in user reviews are of explanation purpose. To solve the problem, we further propose an auto-denoising mechanism based on topical item feature words for sentence generation. Experiments on various e-commerce product domains show that our approach can not only improve the recommendation accuracy, but also the explanation quality in terms of the offline measures and feature words coverage. This research is one of the initial steps to grant intelligent agents with the ability to explain itself based on natural language sentences.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

STaR: Bootstrapping Reasoning With Reasoning (2022, background): “[24] Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang.”
Cross-Loss Influence Functions to Explain Deep Network Representations (2020, background): “…have been proposed, including interpretable neural network approaches (Wang et al., 2016), natural language generation of explanations (Mullenbach et al., 2018; Wiegreﬀe and Pinter, 2019; Chen et al., 2021), and many others Hoﬀman et al. (2018); Holzinger et al. (2020); Linardatos et al. (2021).”