User behavior prediction via heterogeneous information in social networks

With the development of online social networks, user behavior prediction based on the data collected from these social networks has attracted increasing attention. In heteroge-neous social networks, a node usually has several heterogeneous attributes to describe itself from different angles. However, most existing methods only utilize an attribute of each node and neglect other heterogeneous attributes. Therefore, this paper proposes a new user heterogeneous information embedding method, called user heterogeneous infor-mation embedding (UHIE). This method utilizes the attention mechanism to aggregate the heterogeneous attribute information of each neighbor to obtain their low-dimension rep-resentation. Then, the graph neural network is employed to aggregate the multi-relational information from neighbors to obtain the low-dimension representation of nodes. Furthermore, a new soft thresholding method is proposed to eliminate the unimportant information, called multi-head self-attention soft thresholding (MSST), which employs the multi-head self-attention mechanism to calculate an importance threshold for each feature. Based on UHIE and MSST, a new user behavior prediction model is proposed, called Heterogeneous Residual Self-Attention Shrinkage Network (HRSN). This model utilizes UHIE to aggregate heterogeneous information including all heterogeneous attribute infor-mation of nodes, and employs MSST to eliminate unimportant information. The experi-mental results on three real-world datasets show the superiority of the proposed model. (c) 2021 Elsevier Inc. All rights reserved.