Research on recommendation algorithm for geographical location points of interest based on deep learning

Lili Wang; Lili Jiang

doi:10.1117/12.3033108

5 July 2024 Research on recommendation algorithm for geographical location points of interest based on deep learning

Lili Wang, Lili Jiang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318426 (2024) https://doi.org/10.1117/12.3033108
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In order to solve the problem that the traditional point of interest recommendation algorithm will miss user characteristic information, a geographical location point of interest recommendation algorithm based on deep learning was studied. By combining the user's personal historical preference data with spatio-temporal information, taking into account the differences in personal long-term and short-term interests, the user check-in information is classified according to working days and rest days, and the results are calculated using a Voronoi diagram to calculate geographic similarity. Location point of interest recommendation model. This model is compared with algorithms such as RankGeoFM, Caser and NARM, and accuracy, recall and F1 value are selected as evaluation indicators. The experimental results show that this algorithm has obvious advantages in the same test set, under different K values. , are better than other algorithm models and are suitable for recommendation of points of interest in geographical locations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lili Wang and Lili Jiang "Research on recommendation algorithm for geographical location points of interest based on deep learning", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318426 (5 July 2024); https://doi.org/10.1117/12.3033108

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KEYWORDS

Data modeling

Deep learning

Convolution

Data processing

Algorithms

Education and training

Ablation

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