论文标题
通过提示推荐的选择性公平
Selective Fairness in Recommendation via Prompts
论文作者
论文摘要
推荐公平最近引起了极大的关注。在实际系统中，用户通常具有多个敏感属性（例如年龄，性别和职业），并且用户可能不希望其建议结果受这些属性的影响。此外，应在公平意识建模中考虑这些用户属性的哪个以及何时应取决于用户的特定需求。在这项工作中，我们定义了选择性公平任务，用户可以灵活地选择推荐模型无偏见的敏感属性。我们提出了一个新型的基于参数及时的公平感知建议（PFREC）框架，该框架依赖于特定于属性的基于属性的及时偏置消除剂，并具有对抗性训练，从而使选择性公平具有不同的属性组合，以顺序建议。都考虑了特定于任务和特定用户的提示。我们进行广泛的评估，以验证PFREC在选择性公平方面的优势。源代码在\ url {https://github.com/wyqing20/pfrec}中发布。
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}.
