Heterogeneous Neighborhood-Enhanced Graph Contrastive Learning for Recommendation

Heterogeneous self-supervised graph learning has gained considerable attention in recommender systems for its ability to capture diverse semantic and structural relationships in real-world data. Contrastive learning enhances representation learning by maximizing agreement between positive pairs while distinguishing negative ones in cross-views. However, two key challenges remain: 1) noise, such as false negatives, that degrades representation quality; and 2) lack of cross-view alignment causes biased and inconsistent representations. To address these challenges, we propose heterogeneous neighborhood-enhanced graph contrastive learning for recommendation (HNGCL). HNGCL ensures cross-view consistency through alignment and uniformity losses, encouraging embeddings that are both well-aligned and uniformly distributed across views, thereby enhancing generalization and discriminative power. To mitigate noise, HNGCL introduces a neighborhood-enhanced strategy that integrates collaborative neighbors to generate high-quality positive pairs, reducing false negatives and suppressing noise propagation. By leveraging heterogeneous graph structures and cross-view contrastive learning, HNGCL effectively captures intricate semantic and structural patterns, producing robust feature representations. Extensive experiments on real-world datasets demonstrate that HNGCL significantly outperforms state-of-the-art methods in recall and normalized discounted cumulative gain (NDCG), showcasing its effectiveness in overcoming these challenges and advancing recommendation performance. Our code for the model implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangchi107/HNGCL</uri>.