Adaptive Fusion Multi-View Contrastive Learning with Interest Aggregation for Collaborative Filtering

In recent years, recommendation systems based on graph neural networks GNN have achieved remarkable success. Despite their effectiveness, GNN-based methods are often affected by noisy interac tions in user-item data. Consequently, several approaches have adopted graph contrastive learning GCL to address this challenge. However, most existing GCL approaches construct contrastive views from the user-item graph, without explicitly leveraging high-order relational in formation i.e., user-user and item-item relationships . Moreover, they often adopt a uniform perspective on user-item connections, neglecting the diversity of user interests.To address these limitations, we present a graph contrastive recommendation model that incorporates an adaptive multi-view fusion strategy,named AdaFCL. Specifically,to more explic itly exploit high-order information, we design an adaptive fusion mod ule that fuses edge weights derived from both the user-item interaction graph and the high-order collaborative graph i.e., user-user and item item graph .Then this fusion module introduces a learnable generator based on GCN and GAT to generate low-noise contrastive views, as an alternative to traditional random perturbations. Furthermore, we de sign a interest aggregation module to embed users’ personalized prefer ences into the representation learning process.Extensive experiments on three public benchmark datasets demonstrate the superiority of AdaFCL. Compared to the strongest baselines , our model improves performance by up to 9.27 for NDCG@20 and 8.67 for Recall@20.