Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality

TL;DR

This paper introduces a method to enhance explanation path quality in knowledge graph-based recommender systems by considering recency, popularity, and diversity, improving interpretability without sacrificing recommendation accuracy.

Contribution

It proposes quantitative properties for reasoning path quality and combines optimization techniques to improve explanation paths in KG-based recommenders.

Findings

Significant improvement in reasoning path quality metrics

Maintained recommendation accuracy

Applicable across multiple datasets

Abstract

Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allows the underlying model to extract reasoning paths between recommended products and already experienced products from the KG. These paths can be leveraged to generate textual explanations to be provided to the user for a given recommendation. However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation. In this paper, we propose a series of quantitative properties that monitor the quality of the reasoning paths from an explanation perspective, based on recency, popularity, and diversity. We then combine in- and post-processing approaches to optimize for both recommendation quality and reasoning path quality. Experiments on three public data sets show that our approaches significantly increase reasoning path quality according to the proposed properties, while preserving recommendation quality. Source code, data sets, and KGs are available at https://tinyurl.com/bdbfzr4n.