A POI Recommendation Algorithm Based on the Heterogeneous Graph Convolution Network

Abstract

Point-of-interest (POI) recommendation is a type of recommendation task, which generates a list of places that users may be interested in. There is a complex heterogeneous graph structure between users and points of interest. The current recommendation algorithms are generally based on Euclidean space data, and the algorithms based on graph structure also generally use homogeneous graph convolution. To solve these problems, the author proposes a heterogeneous graph convolution network algorithm based on hierarchical subgraphs (HGCNR). By constructing user-centered subgraph layers and interest point-cantered subgraph layers, respectively, the author performs heterogeneous graph convolution on different subgraphs to obtain more effective user node information and interest point node information and form recommendation result. Experiments on two public data sets show that HGCNR can effectively improve the recommendation performance of interest points and achieve better recommendation results.