Prediction Future Location for Multiple account users Across Social Network

Abstract

The personal data of people over social networks bring many opportunities for developing a new application. Some users create multiple accounts on social networks. Correlate user information across social networks helps to design algorithms to predict the future location of multiple accounts of a single individual. Most social media site applications need to share their sites with friends and family and this feature has made many people need to predict the site. Prediction is one of the foremost essential issues that need to be investigated for mobility management In mobile computing systems. In this paper, a proposed algorithm for future location prediction of multiple account users based on using frequent pattern mining. First, apply the frequency algorithm. The proposed algorithm experimented using "Gowalla and foursquare datasets" were presented simple method can be used to infer the user location using publicly available attributes and also the geographic information associated with allocatable friends. We find that it is possible to infer the user city with high accuracy. The proposed algorithm prediction achieved accuracy 96% in the Gowalla dataset when applying the support and confidence for each region that visited the user in a specific time period through some years or many months.