Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) |
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Authors: | Ran Wang Chi-Yin Chow Yan Lyu Victor C. S. Lee Sarana Nutanong Yanhua Li Mingxuan Yuan |
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Affiliation: | 1.Department of Computer Science,City University of Hong Kong,Kowloon,Hong Kong;2.Department of Computer Science,Worcester Polytechnic Institute (WPI),Worcester,USA;3.Huawei Noah’s Ark Lab,Shatin,Hong Kong |
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Abstract: | Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method. |
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