A naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information
[ X ]
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, researchers have been trying to create recommender systems. There are
many diferent 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 diferent 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 diferent recommendation methods. Experimental results confrm that our proposed
method outperforms on Location-based POI recommendation.
Açıklama
Anahtar Kelimeler
Recommendation Algorithms, Collaborative Fltering, Factorization, Big Data Analysis, Location-Based Social Networks, Naïve Bayes Theorem
Kaynak
Multimedia Tools and Applications
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Gültekin, G., & Bayat, O. (2022). A Naïve Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information. Multimedia Tools and Applications, 1-22.
