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Location recommendations for new businesses using check-in data
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Eravci, Bahaeddin, Bulut, Neslihan, Etemoglu, Cagri and Ferhatosmanoglu, Hakan (2016) Location recommendations for new businesses using check-in data. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain, 12-15 Dec 2016. Published in: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) pp. 1110-1117. ISSN 2375-9259. doi:10.1109/ICDMW.2016.0160
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WRAP-location-recommendation-Fehatosmanoglu-2016.pdf - Accepted Version - Requires a PDF viewer. Download (2309Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ICDMW.2016.0160
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.
| Item Type: | Conference Item (Paper) | ||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||
| Library of Congress Subject Headings (LCSH): | Location-based services, Data mining, Social networks -- Mathematical models | ||||
| Journal or Publication Title: | 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) | ||||
| Publisher: | IEEE Computer Society | ||||
| ISSN: | 2375-9259 | ||||
| Book Title: | 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) | ||||
| Official Date: | 2 February 2016 | ||||
| Dates: |
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| Page Range: | pp. 1110-1117 | ||||
| DOI: | 10.1109/ICDMW.2016.0160 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu | ||||
| Grant number: | TUBITAK 2232 grant no: 114C124 | ||||
| Conference Paper Type: | Paper | ||||
| Title of Event: | 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) | ||||
| Type of Event: | Conference | ||||
| Location of Event: | Barcelona, Spain | ||||
| Date(s) of Event: | 12-15 Dec 2016 |
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