PMPRec: A Pre-training encoder based on Meta-Path for Recommendation

Due to the capability of Heterogeneous Information Network (HIN) in modeling different types of entities and relations, HIN based recommendation is proposed to enhance performance. In existing methods, meta-paths are extensively used to explore the semantic information in HINs. However, these methods mainly consider some nodes in instances of meta-paths, which may result in information loss and reduce recommendation performance. In the paper, we propose a Pre-training encoder based on Meta-Path for Recommendation (PMPRec) to learn rich and meaningful information between users and items. Specifically, in the pre-training stage, we design a Meta-Path based encoder (MP-encoder) to properly capture both structural and semantic information between users and items by considering all nodes and edges in instances of meta-paths including their types and positions, leading to effective embeddings. In the fine-tuning stage, we utilize a Multi-Layer Perceptron (MLP) to further optimize the embeddings according to user-item ratings. Experiments on four real-world datasets show the proposed PMPRec model outperforms the baseline models.