让招聘推荐系统同时满足求职者、招聘方需求并可解释。
Explainable Multi-Stakeholder Job Recommender Systems
- 设计兼顾多方利益的可解释推荐机制
- 提升高风险场景下推荐决策的透明度与可信度
- 适合关注AI公平性与透明性的研究者与从业者
近年来，公众对推荐系统的信任度持续下降。随之而来的是立法者日益严格的监管要求，尤其关注隐私、公平性和可解释性等议题，这对推荐系统及人工智能整体提出更高要求。在招聘这类高风险领域，推荐结果直接影响个人职业发展和企业成败，更需确保决策的合理性与透明性。由于系统同时服务求职者、招聘人员和企业三方，各自诉求不同，亟需多利益相关方协同的设计思路。本文总结了我在可解释、多利益相关方招聘推荐系统方面的研究进展，并提出了若干未来研究方向。
原文摘要 · Abstract (English)
Public opinion on recommender systems has become increasingly wary in recent years. In line with this trend, lawmakers have also started to become more critical of such systems, resulting in the introduction of new laws focusing on aspects such as privacy, fairness, and explainability for recommender systems and AI at large. These concepts are especially crucial in high-risk domains such as recruitment. In recruitment specifically, decisions carry substantial weight, as the outcomes can significantly impact individuals' careers and companies' success. Additionally, there is a need for a multi-stakeholder approach, as these systems are used by job seekers, recruiters, and companies simultaneously, each with its own requirements and expectations. In this paper, I summarize my current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.
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