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Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences

Published: 23 May 2024 Publication History

Abstract

Point-of-Interest (POI) recommendation is one of the most important tasks in the research of Location-based Social Networks (LBSNs). To solve the spatial sparsity problem in POI recommendation, this study proposed a recommendation model based on graph autoencoder (GAE) and long short-term preferences applied to location recommendation. Firstly, the POI transfer map of the check-in sequence is constructed, from which the hidden links between each POI are extracted using GAE to alleviate the troubles caused by data sparsity. Secondly, a long short-term preference extraction module is incorporated into the model, considering the influence of user preferences on user decisions. Finally, the proposed GELS model was tested on the Foursquare dataset and compared with results from other models. The GELS model in this study achieved Recall@10 scores of 0.6933 and 0.5099 on two different datasets, surpassing other models and demonstrating its superior performance in POI recommendation. The results show that GELS model can provide new ideas for POI recommendations.

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  1. Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences

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    ICAICE '23: Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering
    November 2023
    1263 pages
    ISBN:9798400708831
    DOI:10.1145/3652628
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 23 May 2024

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