FairCDSR: Fairness-Aware Cross-Domain Sequential Recommendation via Multi-Interest Transfer and Contrastive Learning

Cross-domain sequential recommendation (CDSR) tackles data sparsity and cold-start issues by leveraging information from the source domain to enhance prediction accuracy in the target domain. However, the recommendation fairness issue may further deteriorate dramatically with the biased knowledge transfer of overlapped users. This paper is the first study to address and improve fairness measurement between different demographic groups in CDSR. The proposed FairCDSR employs sequence augmentation techniques to enrich the interaction histories of disadvantaged user groups, which typically have less training data. These augmented sequences are further represented by a contrastive learning method with hard negative sampling to mitigate the unfairness in recommendations. Then, to more precisely capture cross-domain preferences, a multi-interest learning approach is applied to each group across the domains. Finally, an interest-level knowledge transfer algorithm with fixed bandwidth limitations for each group is developed to extract fair and semantic cross-domain information. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of FairCDSR. Compared to existing cross-domain or fair recommendation systems, FairCDSR significantly reduces recommendation disparity between advantaged and disadvantaged groups. Benefiting from a significant improvement in the recommendation accuracy of the disadvantaged group, the overall system performance can also be effectively enhanced by 5-10%.