Abstract: The existing research on the next point-of-interest(POI) recommendation focuses on optimizing the accuracy and practicability of recommendation models by integrating user preferences,sequential behaviors,and spatio-temporal contextual information.However,current recommendation strategies still face two major challenges:1) the dynamic nature of user interests;2) the influence of users' social relations on their decision-making.To address these issues,a next POI recommendation model that integrates dynamic social relations is proposed.This model utilizes a self-attention network to simulate the dynamic changes in user preferences and performs integrated modeling of sequential information,spatio-temporal information,and dynamic social relations.Additionally,two parallel long-term and short-term channels are designed to capture users’ dynamic preferences and context-related dynamic social relations respectively.Through a multi-head self-attention mechanism,it effectively models the long-dependency relationship between any two historical check-in behaviors of users,adaptively allocating contribution values to the next POI.Finally,an attention mechanism is employed in the model's prediction layer to weigh the impact of users’ long-term and short-term preferences as well as their inherent interest in POI on their decision-making.Experiments on real-world and publicly available datasets from Gowalla and Brightkite demonstrate that the proposed model outperforms current next POI recommendation algorithms.

Key words: Next point-of-interest recommendations, Dynamic social relationships, User dynamic preferences, Self-attention networks, User long-term/short-term preferences