Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

Haiqin Ye, Ying Chen

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)20(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9798369324684|DOI: 10.4018/IJSWIS.336921

MLA

Ye, Haiqin, and Ying Chen. "Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN." IJSWIS vol.20, no.1 2024: pp.1-20. http://doi.org/10.4018/IJSWIS.336921

APA

Ye, H. & Chen, Y. (2024). Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-20. http://doi.org/10.4018/IJSWIS.336921

Chicago

Ye, Haiqin, and Ying Chen. "Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN," International Journal on Semantic Web and Information Systems (IJSWIS) 20, no.1: 1-20. http://doi.org/10.4018/IJSWIS.336921

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Abstract

With the intention of addressing the concern that existing point of interest recommendation methods fail to fully utilize the auxiliary information of the point of interest, from which it is challenging to extricate a substantial quantity of deeper feature information, a personalized point of interest (POI) recommendation model using Context-Aware Gated Recurrent Unit (CAGRU) and implicit semantic feature extraction was proposed. First, the check-in data is divided into five tags, and the continuous geographical location check-in data and time data are discretized. Then, the CAGRU was used to obtain the POI check-in features. Finally, the time sequence location information, user information and target location information are transformed through the nonlinear activation function to obtain the score of each location in the data set as the next POI location, and the Top-N recommendation is generated through the score. Experiments indicated that the results of the suggested method were better than the comparative methods.