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We make plans to provid a point-of-interests (POI) recommendation method for location-based social networks (LBSNs) in the paper. LBSNs contain a special three layers network structure based on relevance of location information in the physical world. The available information of three layers of LBSN makes it possible to mine the internal correlations between different layers and to analze users’ preference...
In this paper, we propose a method for point of interest (POI) recommendation by extracting the multi-tag influence and modeling the geographical influence. First of all, we extract a user-tag matrix from the initial user-POI rating matrix by analyzing the relations between POI and the related bag of tags. Secondly, we use the probabilistic factor model to predict the missing data of the extracted...
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