First-order and High-order Information Fusion over Heterogeneous Information Network for Top-N Recommendation System

摘要

In recent years, more and more researchers pay attention to the recommendation system based on heterogeneous information network(HIN), because HIN is rich in various kinds of information, which can significantly improve the performance of the recommendation system. But the HIN based recommendation system faces the following problems: how to leverage high-order information to get semantic-level interaction characteristics between users and items; How to deeply fuse first-order and high-order information to enhance the representation ability of the system. To address these issues, we propose a novel model: First-order and High-order Information Fusion over Heterogeneous Information Network for Top-N Recommender System(FHRec). For first-order information, we use graph neural networks to generate the latent vectors of users and items. And for high-order information, we use a meta-path based semantic-level aggregation to get the interaction between users and items. Then we deeply integrate the first-order and high-order information and use neural collaborative filtering to improve the recommendation performance. Finally, we conduct comparative experiments of our model with other baseline algorithms on three real world datasets, and the experimental results prove the superiority of our model.