Attributed Heterogeneous Information Network Embedding with Self-Attention Mechanism for Product Recommendation

Heterogeneous network embedding based recommendation technology has the capability to capture the structural information in the network effectively, thus improving the recommendation performance. However, the existing recommendation technology based on heterogeneous network embedding not only ignores the attribute information of nodes and various types of edge relations between nodes, but also ignores the diverse influences of different nodes’ attribute information on recommendation results. To address the above issues, a product recommendation framework based on attributed heterogeneous information network embedding with self-attention mechanism (AHNER) is proposed. The framework utilizes attributed heterogeneous information network embedding to learn the unified low-dimensional embedding representations of users and products. When learning node embedding representation, considering that different attribute information has different effects on recommendation results and different edge relations between nodes reflect users’ different preferences for products, self-attention mechanism is exploited to mine the latent information of node attribute information and different edge types and learn attribute embedding representation is learned. Meanwhile, in order to overcome the limitation of traditional dot product method as matching function, the framework also exploits deep neural network to learn more effective matching function to solve the recommendation problem. We conduct extensive experiments on three public datasets to evaluate the performance of AHNER. The experimental results reveal that AHNER is feasible and effective.