Selective Fairness in Recommendation via Prompts

TL;DR

This paper introduces PFRec, a prompt-based framework that allows users to specify which sensitive attributes should be free of bias in sequential recommendation systems, enhancing fairness customization.

Contribution

It proposes a novel prompt-based approach for selective fairness in recommendation, enabling flexible bias elimination for different user-specified attributes.

Findings

PFRec achieves superior performance in selective fairness tasks.

The framework effectively handles multiple sensitive attributes.

Source code is publicly available for reproducibility.

Abstract

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in \url{https://github.com/wyqing20/PFRec}.