Heterogeneous Graph Neural Network for Recommendation
This work addresses the need for better recommendation systems in e-commerce by leveraging heterogeneous graph structures, though it appears incremental as it builds on existing meta-path and attention methods.
The paper tackles the problem of learning representative node embeddings for personalized recommendation systems by modeling interactions as heterogeneous graphs, proposing HGRec which aggregates multi-hop meta-path neighbors and fuses semantics via attention, with experimental results showing improved performance and interpretability.
The prosperous development of e-commerce has spawned diverse recommendation systems. As a matter of fact, there exist rich and complex interactions among various types of nodes in real-world recommendation systems, which can be constructed as heterogeneous graphs. How learn representative node embedding is the basis and core of the personalized recommendation system. Meta-path is a widely used structure to capture the semantics beneath such interactions and show potential ability in improving node embedding. In this paper, we propose Heterogeneous Graph neural network for Recommendation (HGRec) which injects high-order semantic into node embedding via aggregating multi-hops meta-path based neighbors and fuses rich semantics via multiple meta-paths based on attention mechanism to get comprehensive node embedding. Experimental results demonstrate the importance of rich high-order semantics and also show the potentially good interpretability of HGRec.