Published on Sat Jan 09 2021

Generate Natural Language Explanations for Recommendation

Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang

A great challenge of explanation generation in E-commerce recommendation is that not all sentences in user reviews are of interest. We propose a hierarchical sequence-to-sequence model (HSS) for explanation generation. We further propose an auto-denoising topical item feature words for sentence generation.

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Abstract

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.

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Textual explanations have proved to help improve user satisfaction on machine-made recommendations. Current mainstream solutions loosely connect the learning of explanation with the learning of recommendation. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation.
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Algorithm predicts not only a user's preference over a list of. items, i.e., recommendation, but also how the user would appreciate a. feature level. Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize.
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