Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach

arXiv:2405.01063v1 Announce Type: cross
Abstract: As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes becomes an imperative requirement. Prior approaches for enhancing fairness in recommender systems presume the availability of all sensitive attributes, which can be difficult to obtain due to privacy concerns or inadequate means of capturing these attributes. In practice, the efficacy of these approaches is limited, pushing us to investigate ways of promoting …

abstract arxiv availability commerce cs.cy cs.ir cs.lg domains e-commerce fair fairness job optimization prior recommendations recommender systems robust searching systems type