Knowledge-grounded Natural Language Recommendation Explanation
Anthony Colas Jun Araki Zhengyu Zhou Bingqing Wang Zhe Feng
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.