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First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods.<\/p>","DOI":"10.4018\/ijwsr.2021100103","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T18:26:22Z","timestamp":1632853582000},"page":"51-74","source":"Crossref","is-referenced-by-count":8,"title":["A Graph Neural Network-Based Algorithm for Point-of-Interest Recommendation Using Social Relation and Time Series"],"prefix":"10.4018","volume":"18","author":[{"given":"Mingjun","family":"Xin","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}]},{"given":"Shicheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}]},{"given":"Chunjuan","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}]}],"member":"2432","reference":[{"key":"IJWSR.2021100103-0","unstructured":"Diederik, P. 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