Next POI recommendation for random group based on Spatio-Temporal heterogeneous graph

Next Point-of-Interest (POI) recommendations for random groups are challenging due to the instability of member relationships and the dynamic evolution of member preferences. To address the issues above, this work proposes a novel Next POI recommendation for Random Group based on Spatio-Temporal Heterogeneous Graph (named as NPRRG-STHG) model. Specifically, NPRRG-STHG constructs a spatio-temporal heterogeneous graph and uses HNode2Vec to learn members’ multidimensional preferences. Next, NPRRG-STHG balances preference differences among group members and generates a fitted representation of the random group. Meanwhile, NPRRG-STHG learns comprehensive POI representations from spatio-temporal enhanced POI interaction graphs and POI transfer graphs using Edge-Enhanced Bipartite Graph Neural Network (EBGNN) and Spatio-Temporal Graph Convolutional Network (STGCN) models, respectively. Finally, NPRRG-STHG recommends the next POI that best matches the random group’s overall preferences. We validated NPRRG-STHG on three public benchmark datasets (Foursquare, Gowalla, and Yelp) with 124,933 to 860,888 check-in records. Compared to advanced baselines, NPRRG-STHG achieved average improvements of about 21.4% in Precision@K and 36.7% in NDCG@K. Ablation studies further verify the effectiveness of each component. These results demonstrate that NPRRG-STHG provides an effective solution for next POI recommendations in random groups.