Based Point of Interest and Experience to Task Assignment on Location-Based Social Networks

Abstract

In recent years, with the popularity of smart mobile devices, mobile Internet has rapidly developed. When the social network meets the localization technology, it gives birth to a Location-Based Social Network (LBSN). The situational awareness based on the location has become more research significance. However, how to combine the context awareness, mobile sensors and abundant users' historical location data to make the platform more efficient, how to ensure that the proceeds can improve the accuracy of recommendation and perceived task assignment, are still challenges in the location-based social network. In this paper, we demonstrate a model to use historical location data of the participants and analyze the point-of-interest (POI). Then we propose the user location and the empirical value algorithm PTHS based on the HITS algorithm. Through analyzing the interest points of the selected scene perception task, we find that those users have similar point-of-interest, and rank them by the location and experience PTHS algorithm. Finally, much more appropriate users are assigned to the tasks. Through theoretical analysis and extensive simulations, we validate that proposed method is effective and efficient.