Heterogeneous Information Network-based Interest Composition with Graph Neural Network for Recommendation
This work addresses a bottleneck in recommendation systems for users by enhancing interest composition, though it appears incremental as it builds on existing HIN and GNN methods.
The paper tackled the problem of limited performance in heterogeneous information network-based recommendation systems by proposing HicRec, a model that composes user interests from multiple meta-paths, achieving improved results over baselines on three real-world datasets.
Heterogeneous information networks (HINs) are widely applied to recommendation systems due to their capability of modeling various auxiliary information with meta-paths. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits performance improvement because it lacks the capability of interest compositions among meta-paths. In this article, we propose an HIN-based Interest Composition model for Recommendation (HicRec). Specifically, user and item representations are learned with a graph neural network on both the graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure that the user and item representations are in the same latent space. Then, users' interests in each item from each pair of related meta-paths are calculated by a combination of the user and item representations. The composed user interests are obtained by their single interest from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate that our proposed HicRec model outperforms the baselines.