A POI Clustered Users Recommendation Method in LBSNs Considering the Weather Forecast

Abstract

The Location-Based Social Network (LBSN) is one type of social web platform that allows users to register when they visit certain Points of Interest (POIs). The practice of recommending points of interest (POI) has become an important component of location-based social networks. However, due to the unique limits such as privacy concerns, accuracy, and reliability, and lack of comprehensive POI coverage of these networks, it remains a difficult challenge. In this thesis, data mining methods (i.e., model-based approach) are applied to handle the problem of the POI recommendation system. The approach is based on a particular method of analyzing geographic data. POI recommendations have a hard time (because of limited context information, credibility concerns, and lack of personalization) persuading users to visit the suggested destinations. The major purpose of this study is to categorize locations into different areas and to use the user group check-ins to train the model to forecast the weather using classification models. In this study, two datasets including Foursquare Check-Ins Tokyo Dataset, and Foursquare Check-Ins New York Dataset are used which contain the check-ins of different locations in New York and Tokyo from 2012-2016. The user’s check-in history is used in terms of timestamps, longitude, latitude, and user behavior to analyze the reliably forecast of the user’s location weather. After applying the data mining methods, the results demonstrate that for the New York check-in corpus, the decision tree method obtained the top performance with an accuracy of 0.74. Regarding the Tokyo check-in corpus, the overall performance indicates that the decision tree algorithm achieved the maximum accuracy of 0.84. The results concerning weather classification show that random forest achieved the best performance with an accuracy of 0.95