Meta-path Embedding based Recommendation over Heterogeneous Information Network

Abstract

Heterogeneous information network (HIN) based models aim to make recommendations as much as possible using meta-path information. It would be more advisable if they consider the scenario information provided by meta-path and explore the relatedness of meta-paths. To address these issues, we propose an End-to-end model based on Meta-Embedding for the Recommendation over HIN, called EMER. Here, meta-embedding refers to the explicit representation of meta-path under the user and item pair. Learning the meta-embedding can capture the scenario information provided by meta-path, and can directly explore the relatedness between meta-paths. Furthermore, we leverage the meta-embedding to design three loss functions of structure loss, relation loss and rating loss to capture three aspect information. We use structure loss to calculate the similarity between nodes based on meta-embedding to approximate the similarity calculated based on meta-path for preserving the structural characteristics. The relation loss models meta-paths by interpreting them as translations operating on the embeddings of users and items. We use rating loss to characterize the difference between the predicted ratings and the truth. Experiments over three real datasets indicate that our model achieves state-of-the-art performance.