Intent-Aware Contrastive Learning for Cross-Domain Recommendation

Accurately capturing user preferences across diverse domains is a fundamental challenge in cross-domain recommendation (CDR) systems. Recent literature has established that disentangling user preferences into global and domain-specific components significantly enhances recommendation performance. However, existing CDR systems are hindered by two critical challenges: 1) how to align user representations across domains to accommodate the discrepancy of user intents in different domains; and 2) how to mitigate data conflicts caused by cross-domain heterogeneity. To address these limitations, we propose intent-aware contrastive learning for cross-domain recommendation (ICCDR). This framework learns unified user interests across domains while mitigating data conflicts from heterogeneity. The ICCDR framework consists of the following core components: 1) a cross-intent module that dynamically identifies users’ consistent intents across domains to guide the transfer of knowledge; and 2) an information bottleneck-based aggregation module designed to mitigate data conflicts by filtering out task-irrelevant information, thereby retaining only the essential latent features required for effective knowledge transfer. Extensive experiments conducted on Amazon datasets demonstrate that the ICCDR framework outperforms traditional baseline methods.