Recently, the research on fairness in recommendation systems has garnered widespread attention. Moreover, numerous fair recommendation models have been developed for scenarios with limited sensitive information, thereby alleviating the issue of missing sensitive information. However, the performance of these methods still tends to decline significantly when sensitive attributes are extremely scarce. In this paper, we propose FairCL, a novel fair recommendation framework designed to perform effectively under limited sensitive attribute information. FairCL features a contrastive learning-based sensitive attribute encoder that can be integrated with existing fair recommendation algorithms. By leveraging both collaborative information and item side information, we predict unknown sensitive attributes and apply contrastive learning for sensitive attribute modeling. Furthermore, we theoretically demonstrate how FairCL can be integrated with mutual information-based and adversarial learning-based fairness algorithms. Extensive experiments on three real-world datasets show that FairCL significantly enhances fairness, even when only a small portion of users' sensitive attributes are known. The code and data are at: https://anonymous.4open.science/r/CL-for-FairRec-000A/.

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.