Generate Natural Language Explanations for Recommendation

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.

Accepted to the SIGIR 2019 Workshop on ExplainAble Recommendation and Search, Paris, France, July 2019

References in corpus (10)

• Learning over Knowledge-Base Embeddings for Recommendation
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
• Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
• Topic Aware Neural Response Generation
• Neural Rating Regression with Abstractive Tips Generation for Recommendation
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• Learning through Dialogue Interactions by Asking Questions
• Context-aware Natural Language Generation with Recurrent Neural Networks
• Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

Cited by in corpus (9)

• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• Explainable Fairness in Recommendation
• Counterfactual Collaborative Reasoning
• Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
• Personalized Transformer for Explainable Recommendation
• EXTRA: Explanation Ranking Datasets for Explainable Recommendation
• Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation
• A Web Scale Entity Extraction System
• Query Understanding via Intent Description Generation