Intent-aware Graph Neural Network for Point-of-Interest embedding and recommendation

Abstract

Point of Interest (POI) recommendation algorithms can help users find the POIs that they prefer, and they can also help merchants to find potential customers. However, most existing methods still have difficulties effectively utilizing the information in users’ check-in data. Significantly, they ignore the intent behind the users’ check-in behaviors, which limits the recommendation performance. In this paper, we propose an Intent Aware Graph Neural Network-based model(IAGNN) to predict/recommend the next POI with which the target user may interact. Specifically, IAGNN first models the user’s check-in behavior sequences as graphs and utilizes the information transmission mechanism of the graph neural network (GNN) to learn the feature vector representation (embedding) of POIs. Second, we devise a hierarchical attention network for capturing users’ preferences adaptively. At the same time, we design a user intent-aware module based on disentangled representations to extract the user’s intents. Finally, the user’s preferences and their intents obtained by the user intent perception module are combined to recommend the POI for the user. Extensive evaluations are conducted on two real-world POI check-in datasets. The experimental results show that our proposed model IAGNN outperforms the baselines in terms of both recall and MRR.