A Multi-Level Contrastive Learning Framework for Knowledge Graph-Based Recommendation Systems

In recent years, researchers have introduced knowledge information and structural information extracted from knowledge graphs into recommendation systems to improve their performance. However, existing knowledge graph-based recommendation algorithms face challenges in extracting sufficient knowledge and identifying noise unrelated to the recommendation task. To address the aforementioned challenges, we introduce a multi-level contrastive learning framework for knowledge graph-based recommendation systems, MCKGRec. The model integrates information from three levels: user-item interactions, item-entity knowledge, and collaborative knowledge structures. It utilizes lightweight graph convolutional networks to capture interactive signals, graph attention networks to learn knowledge information, and a collaborative knowledge propagation module to capture global structural information. Additionally, a multi-level contrastive learning task is introduced to enhance the recommendation accuracy and robustness. Comprehensive testing across three different datasets confirms that our method significantly enhances the efficacy of recommendation outcomes. By leveraging knowledge graph information and multi-level contrastive learning, MCKGRec better captures complex user-item relationships and filters out irrelevant noise.