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      • Department of Computer Engineering
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      Location recommendations for new businesses using check-in data

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      Author(s)
      Eravci, Bahaeddin
      Bulut, Neslihan
      Etemoğlu, C.
      Ferhatosmanoğlu, Hakan
      Date
      2016-12
      Source Title
      IEEE International Conference on Data Mining Workshops, ICDMW
      Publisher
      IEEE
      Pages
      1110 - 1117
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.
      Keywords
      Business decision systems
      Location based social networks
      Spatio-temporal data mining
      Bayesian networks
      Collaborative filtering
      Customer satisfaction
      Data mining
      Filtration
      Inference engines
      Social networking (online)
      Bayesian inference
      Business decisions
      Business opportunities
      Location-based social networks
      Mobile applications
      Potential customers
      Probabilistic approaches
      Location
      Permalink
      http://hdl.handle.net/11693/37627
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ICDMW.2016.0160
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      • Department of Computer Engineering 1510
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