HEK-CL: Hierarchical Enhanced Knowledge-Aware Contrastive Learning for Recommendation

Recently, there has been an emergence of self-supervised recommendation methods that integrate knowledge graphs. Upon conducting a comprehensive review of contrastive learning (CL) in recommender systems, we conclude that existing methods solely focus on data view generation (the first phase) while neglecting the equally pivotal data view alignment (the second phase). However, due to the complexity and variability of real-world graph data, regardless of the graph augmentation strategy employed, it may be unrealistic to expect all entities to benefit from contrastive learning. In this paper, we propose a H ierarchical E nhanced K nowledge-Aware C ontrastive L earning (HEK-CL) method for recommendation. Overall, we aim to hierarchically carry out enhancement strategies in both the first and second phases of knowledge-aware contrastive learning: 1) From the perspective of enhancing data view generation, we focus on combining non-Euclidean representation learning with graph denoising modules. Owing to the unified space's ability to learn the ideal curvature from data distributions, the quality of embeddings for graph data has seen enhancements; 2) From the perspective of enhancing data view alignment, we propose a hyperbolic robust contrastive loss, named HRCL. Through rigorous theoretical analysis and experiments, we demonstrate that HRCL provides a more balanced and equitable training process for all entities than InfoNCE. Numerous experiments on the three real-world datasets show that our HEK-CL outperforms state-of-the-art baselines.