ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

TL;DR

This paper introduces ELIXIR, a human-in-the-loop framework that uses user feedback on explanations to improve recommendation quality, demonstrating significant gains in movie and book recommendations.

Contribution

It presents a novel approach to incorporate explanation feedback into recommendation models, enhancing personalization and trustworthiness.

Findings

Significant improvement in recommendation accuracy.

Effective use of explanation feedback for preference learning.

Overcoming data sparsity with label propagation.

Abstract

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of the generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.