Explanation as a Defense of Recommendation

TL;DR

This paper introduces a sentiment alignment approach that tightly couples recommendation and explanation generation, improving explanation quality and user understanding in recommendation systems.

Contribution

It proposes a novel sentiment alignment method linking recommendation and explanation modules via a latent sentiment vector, enhancing explanation relevance and system transparency.

Findings

Outperforms baselines in recommendation accuracy

Generates higher quality explanations

Enhances user understanding of recommendations

Abstract

Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.