User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

TL;DR

This paper introduces a user-controllable recommender system that combines explainability with counterfactual reasoning, allowing users to customize recommendations and potentially improve accuracy.

Contribution

It presents a novel framework integrating retrospective and prospective explanations for enhanced user control in recommender systems.

Findings

Effective user control demonstrated on MovieLens and Yelp datasets.

Controllability attributes include complexity and accuracy, with positive impacts.

User control options can potentially enhance recommendation accuracy.

Abstract

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.