URPI-GRU: An approach of next POI recommendation based on user relationship and preference information

Next POI (Point of Interest) recommendation aims to recommend next POI for users at specific time given users' historical check-ins. User relationship and preference information are important factors that can affect the user's decision-making behavior on the next POI. To this end, we propose an approach for the next POI recommendation based on user relationship and preference information, called URPI-GRU (User relationship and Preference information Gated Recurrent Unit). URPI-GRU contains two modules, short-term module and long-term module. First, we construct a user relationship graph and learn user relationship vectors. And then we divide the check-ins into current preference, periodic preference and long-term preference according to the user's check-in time. In the short-term module, the user's periodic preference and current preference are learned through the GRU model, and they are concatenated with the user relationship vector to learn the short-term scores of POIs. In the long-term module, the user's long-term preference is mined through the K-nearest neighbor sequences to obtain the long-term scores of the POIs. Last, we recommend the POIs based on the total score of short-term and long-term scores. Extensive experiments on two representative real-world datasets demonstrated that our model yields significant improvements over the state-of-the-art methods. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an approach for next POI recommendation based on user relationship and preference information. By learning user relationship vectors and combining short-term and long-term modules, the method obtains scores for POIs and achieves significant improvements in user recommendation, as demonstrated by extensive experiments.