Point-of-interest Recommendation Based on Geospatial-TemporalCorrelations and Social Influence

With the popularity of location-based social networks,personalized POI recommendation has become an important task.However,existing research inadequately considers the intrinsic relationships when modeling and integrating contextual information.Among these,geographical and temporal contexts often interact with and influence each other.Moreover,when mode-ling user social relationships,current approaches primarily measure the direct similarity between POI subsets visited by different users to express the similarity of their social behaviors.These approaches fails to effectively alleviate the impact of data sparsity on measuring the similarity of POI subsets visited by different users.Therefore,by reasonably reconstructing the contextual information model and effectively integrating it into user preference model,a POI recommendation method based on geospatial-temporal correlations and social influence is proposed.This method leverages the spatial aggregation phenomenon of users' primary geographic activity centers under different temporal states.It employs a time-constrained approach to assess the geographical correlations between POIs,thereby modeling the impact of POI geographical information on user check-ins.Furthermore,when mo-deling user social relationships,it is assumed that users with more shared check-ins at POIs or greater overlap in the categories of POIs they visit exhibit higher similarity in their social behaviors.By incorporating POI category information,the effectiveness and utility of social relationship modeling are enhanced.Finally,the proposed geospatial-temporal correlation model and user social relationship model are integrated into a weighted matrix factorization framework to perform personalized POI recommendations for users.Extensive experiments demonstrate that the proposed method achieves superior POI recommendation performance,highlighting the advantages of the proposed models in contextual modeling and overcoming data sparsity.