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Haiying Che, Di Sang, Billy Zimba. Recommending Personalized POIs from Location Based Social Network[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(1): 137-145. doi: 10.15918/j.jbit1004-0579.201827.0117
Citation: Haiying Che, Di Sang, Billy Zimba. Recommending Personalized POIs from Location Based Social Network[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(1): 137-145.doi:10.15918/j.jbit1004-0579.201827.0117
shu

Recommending Personalized POIs from Location Based Social Network

doi:10.15918/j.jbit1004-0579.201827.0117

  • School of Software, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2017-02-08
    Abstract
  • Location based social networks (LBSNs) provide location specific data generated from smart phone into online social networks thus people can share their points of interest (POIs). POI collections are complex and can be influenced by various factors, such as user preferences, social relationships and geographical influence. Therefore, recommending new locations in LBSNs requires to take all these factors into consideration. However, one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data, or from the user friend information, or based on the different geographical influences on each user's check-in activities. In this paper, we propose an algorithm that calculates the user similarity based on check-in records and social relationships, using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition, a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user's check-in locations. Experimental results, using foursquare datasets, have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall, furthermore solving the sparsity problem.
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