Most of the existing work on session-based recommendation has considered leveraging neighborhoods to refine the target session representation for improving the recommendation performance. However, the potential of neighborhoods is still not fully exploited due to two main limitations: First, most existing methods tend to overlook the cooperative relationships between neighborhoods derived from different perspectives. Second, they often fail to preserve the self-anchoring property of the current session representations when integrating neighborhoods from multiple perspectives. To address these limitations, we propose a novel session-based recommendation framework named CooSBR. Specifically, this proposed model consists of two core components: the neighbor cooperation module and the session-centric diffusion enhancement module. In the neighbor cooperation module, mutual contrastive learning directly models the cooperative relationship between neighborhood representations from different perspectives, while pivot contrastive learning indirectly strengthens this cooperation by aligning each neighborhood view with a pivot embedding that integrates the target session and that view. In the session-centric diffusion enhancement module, a multi-conditional diffusion process is introduced to progressively integrate multi-perspective neighborhood information, while maintaining the inherent semantics of the session and preserving its self-anchoring property. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of CooSBR, yielding average improvements of 5.10% (HR@10), 5.25% (HR@20), 8.80% (MRR@10), and 8.95% (MRR@20).
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