Knowledge-grounded Natural Language Recommendation Explanation

TL;DR

This paper introduces a knowledge graph-based method for generating fact-grounded, personalized natural language explanations in recommendation systems, improving interpretability and user trust.

Contribution

It proposes a novel collaborative filtering-based knowledge graph approach that jointly learns user-item representations for recommendation and explanation generation.

Findings

Outperforms previous models on natural language explainable recommendation tasks.

Produces fact-grounded explanations based on item features and user preferences.

Enhances recommendation interpretability and user trust.

Abstract

Explanations accompanied by 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.