A naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information

Gültekin, GünayBayat, Oğuz2022-02-022022-02-022022Gültekin, 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.https://hdl.handle.net/20.500.12939/2235In 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.eninfo:eu-repo/semantics/closedAccessRecommendation AlgorithmsCollaborative FlteringFactorizationBig Data AnalysisLocation-Based Social NetworksNaïve Bayes TheoremA naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual informationArticle1222-s2.0-85123240803Q1WOS:000744767900003Q2