Heterogeneous Information Network Recommendation Driven by Automatic Meta-Path Identification

Heterogeneous information networks significantly enhance the performance of recommendation systems by integrating rich structural and semantic information. However, most existing methods are designed for homogeneous networks and utilize techniques such as attention mechanisms and multi-layer architectures, which often introduce unnecessary parameter complexity. Furthermore, meta-path-based approaches typically rely on manually predefined meta-paths, which limit the models’ generalization capabilities. This article conducts an in-depth investigation into meta-path identification and proposes an Automatic Meta-Path Identification Recommendation (AMPIRec) framework. The framework optimizes the semantic representation of meta-paths through matrix self-transformation properties using weighted aggregation, while simultaneously reducing model complexity. Additionally, AMPIRec employs a multi-head attention mechanism for the joint training and optimization of user and item features, with its primary advantages being numerical stability and high prediction accuracy. To enhance recommendation precision, we specifically design a tailored top-k smoothed loss function that aligns recommendation objectives with real-world requirements. Extensive experiments on multiple real-world heterogeneous graph datasets demonstrate that AMPIRec achieves outstanding performance in both accuracy and stability.