Research And Application Of User Location Services Algorithms In Social Network

With the popularity of smart phones and GPS devices, an obvious trend is to integrate traditional location-based services and social networks, thereby forming location-based social network. Point of Interest(POI) as location information is the most critical part, to a certain extent reflects the concentration of the urban population of the region. How to mine POI's features becomes one of the hot spots at home and abroad.In this thesis, it takes GPS trajectory data in location services and POI in social network as the research object.Based on existing research techniques for the LBS, the thesis presents a path matching algorithm based on grid, and the density clustering model is constructed by the POI in social network. Finally, as the application target of achieve the intelligent taxi recommendation system, to enhance the quality of LBS services. The main work and innovation of this thesis are as follows:1. Firstly, aiming at the problem of GPS sampling error, based on the existing ST_Matching path matching algorithm, a new path matching algorithm based on grid is proposed. The algorithm for the GPS sampling points grid, just consider the road network within the grid, to reduce the search space, Meanwhile, the sliding window is calculated global optimal matching path, improve mining efficiency, and experimental analysis by comparing its performance advantages.2. Secondly, analysis of the characteristics of POI in social network is down. A density based clustering algorithm of POI is proposed, to find the hot areas, used in intelligent taxi recommendation system, to assist to find the candidate point position.3. Thirdly, on the basis of the above research, a taxi recommendation system based on GPS trajectory is developed, to analyze historical GPS trajectory data. Different sections of empty taxi arrival time modeling, to use POI to find popular areas, and establish a forecast accurate, recommend reasonable taxi recommendation system with user query services, to provide users with intelligent travel advice. The validity and reliability of the system are verified by the actual data.