Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning.

ABSTRACT: Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical applications. To address this challenge, this study proposes a knowledge-aware recommendation algorithm framework that incorporates multi-level contrastive learning. This framework enhances the Collaborative Knowledge Graph (CKG) through a random edge dropout method, which constructs feature representations at three levels: user-user interactions, item-item interactions and user-item interactions. A dynamic attention mechanism is employed in the Graph Attention Networks (GAT) for modeling the KG. Combined with the nonlinear transformation and Momentum Contra

SUBMITTER: Rong Z 

PROVIDER: S-EPMC11452630 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature