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Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism: Personalized Location-Based Service

Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism: Personalized Location-Based Service

Xue Yu
Copyright: © 2022 |Volume: 34 |Issue: 3 |Pages: 21
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799893264|DOI: 10.4018/JOEUC.294902
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MLA

Yu, Xue. "Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism: Personalized Location-Based Service." JOEUC vol.34, no.3 2022: pp.1-21. http://doi.org/10.4018/JOEUC.294902

APA

Yu, X. (2022). Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism: Personalized Location-Based Service. Journal of Organizational and End User Computing (JOEUC), 34(3), 1-21. http://doi.org/10.4018/JOEUC.294902

Chicago

Yu, Xue. "Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism: Personalized Location-Based Service," Journal of Organizational and End User Computing (JOEUC) 34, no.3: 1-21. http://doi.org/10.4018/JOEUC.294902

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Abstract

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.