Path-based summary explanations for graph recommenders (extended version)

TL;DR

This paper introduces a novel approach for generating summary explanations for graph recommenders, focusing on collective user and item behaviors, using efficient graph algorithms to improve interpretability and usefulness.

Contribution

It proposes a new method for summarizing explanations in graph recommenders with Steiner Tree algorithms, enhancing explanation clarity and insightfulness.

Findings

Summaries are more concise and informative than baseline methods.

The approach outperforms existing explanation methods across multiple metrics.

Evaluations show improved comprehensibility and usefulness of explanations.

Abstract

Path-based explanations provide intrinsic insights into graph-based recommendation models. However, most previous work has focused on explaining an individual recommendation of an item to a user. In this paper, we propose summary explanations, i.e., explanations that highlight why a user or a group of users receive a set of item recommendations and why an item, or a group of items, is recommended to a set of users as an effective means to provide insights into the collective behavior of the recommender. We also present a novel method to summarize explanations using efficient graph algorithms, specifically the Steiner Tree and the Prize-Collecting Steiner Tree. Our approach reduces the size and complexity of summary explanations while preserving essential information, making explanations more comprehensible for users and more useful to model developers. Evaluations across multiple metrics demonstrate that our summaries outperform baseline explanation methods in most scenarios, in a variety of quality aspects.