Graph-based Extractive Explainer for Recommendations

TL;DR

This paper introduces a graph attentive neural network model for extractive explanations in recommender systems, aiming to improve the clarity, reliability, and personalization of explanations by integrating multiple data sources and optimizing sentence selection.

Contribution

It proposes a novel graph-based neural network that combines user, item, attributes, and sentences, along with an ILP-based method for selecting the most relevant explanations.

Findings

Outperforms state-of-the-art baselines on benchmark datasets

Generates more perceivable and personalized explanations

Achieves higher relevance and coverage in explanations

Abstract

Explanations in a recommender system assist users in making informed decisions among a set of recommended items. Great research attention has been devoted to generating natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes, and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage, and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of the proposed solution.