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A Naïve Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information

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Abstract

In recent years, researchers have been trying to create recommender systems. There are many different recommender systems. Point of Interest (POI) is a new type of recommender systems that focus on personalized and context-aware recommendations to improve user experience. Recommender systems use different types of recommendation methods to obtain information on POI. In this research paper, we introduced a Naïve Bayes Prediction Model based on Bayesian Theory for POI recommendation. Then, we used the Brightkite dataset to make predictions on POI recommendation and compared it with the other two different recommendation methods. Experimental results confirm that our proposed method outperforms on Location-based POI recommendation.

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Acknowledgements

This research paper is made possible through the help and support from everyone, including family, parents, teachers, friends, and my working company and, in essence, all sentient beings. Especially, please allow me to dedicate my acknowledgement of gratitude toward the following significant advisors and contributors:

First and foremost, I would like to thank my instructor, Director of Graduate School of Science and Engineering, Advisor to President, Prof. Dr. Oğuz Bayat, for his most support and encouragement for giving me this research.

Finally, I sincerely thank my wife, my parents, and friends, who provide a working environment, and guidance. This product of this research paper would not be possible without all of them.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Altinbas University, İstanbul, Turkey

    Günay Gültekin & Oğuz Bayat

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  1. Günay Gültekin
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  2. Oğuz Bayat
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Correspondence to Günay Gültekin.

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Gültekin, G., Bayat, O. A Naïve Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information. Multimed Tools Appl 81, 6957–6978 (2022). https://doi.org/10.1007/s11042-021-11676-4

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  • DOI: https://doi.org/10.1007/s11042-021-11676-4

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