Where to go Next ? Social and Spatio-Temporal Learning for Next Points-of-Interest Prediction Using Residual Vision Transformer

Recommendation engines based on location help users find visually appealing Points-of-Interest (POI). Next POIs recommendation is very significant and advantageous. In this paper, we propose a network for next POIs prediction, called “Where to Go Next (WTG)”, employing a hybrid deep learning model based on residual neural network and vision trans-former. WTG method models multiple features, including spatio-temporal, sequential, social, and context features. Users' mobility behavior is first modeled as images and a new architecture is then used to capture the spatio-temporal patterns while taking into account the social relations connecting users. The sequential features that are necessary to characterize the user behaviors are then extracted using an the vision transformer. Moreover, context feature extraction is integrated into the model. We demonstrate our model's significant superiority over eight existing methods by evaluating it on two real location-based networks: Foursquare and Gowalla. The source code supporting the findings of this study can be accessed and reproduced using the materials provided at the following repository: https://anonymous.4open.science/r/WTG-Next-ASCC.