Recommendation on Heterogeneous Information Network with Type-Sensitive Sampling

Most entities and relations for recommendation tasks in the real world are of multiple types, large-scale, and power-law. The heterogeneous information network (HIN) based approaches are widely used in recommendations to model the heterogeneous data. However, most HIN based approaches learn the latent representation of entities through meta-path, which is predefined by prior knowledge and thus limits the combinatorial generalization of HIN. Graph neural networks (GNNs) collect and generalize the information of nodes on the receptive field, but most works focus on homogeneous graphs and fail to scale up with regard to power-law graphs. In this paper, we propose a HIN based framework for recommendation tasks, where we utilize GNNs with a type-sensitive sampling to handle the heterogeneous and power-law graphs. For each layer, we adopt schema-based attention to output the distribution of sampling over types, and then we use the importance sampling inside each type to output the sampled neighbors. We conduct extensive experiments on four public datasets and one private dataset, and all datasets are selected carefully for covering the different scales of the graph. In particular, on the largest heterogeneous graph with 0.4 billion edges, we improve the square error by 2.5% while yielding a 26% improvement of convergence time during training, which verifies the effectiveness and scalability of our method regarding the industrial recommendation tasks .