Generate natural language explanations for recommendation
Proceedings of SIGIR’19 Workshop on ExplainAble Recommendation and Search. ACM(2019)
摘要
Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the e ectiveness, transparency, persuasiveness and trustworthiness of recommender systems. Current explainable recommendation models mostly generate textual explanations based on pre-de ned sentence templates. However, the expressiveness power of template-based explanation sentences are limited to the pre-de ned expressions, and manually de ning the expressions require signi cant human e orts. 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. Di erent 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 o ine measures and feature words coverage. is 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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