Explainable Entity-based Recommendations with Knowledge Graphs

TL;DR

This paper presents a method for generating explainable recommendations using knowledge graphs, especially when review texts are unavailable, by jointly ranking items and entities with Personalized PageRank.

Contribution

It introduces a novel approach that leverages external knowledge graphs to generate explanations without relying on review content.

Findings

Effective in scenarios lacking review data

Joint ranking of items and entities improves explanation quality

Utilizes Personalized PageRank for recommendation and explanation generation

Abstract

Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.