Location Recommendations for New Businesses Using Check-in Data

• Published in: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) pp. 1110 - 1117
• Main Authors: Eravci, Bahaeddin, Bulut, Neslihan, Etemoglu, Cagri, Ferhatosmanoglu, Hakan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Bayes methods; Business; business decision systems; Collaboration; Correlation; location based social networks; Nickel; Probabilistic logic; spatio-temporal data mining; Urban areas
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW.2016.0160

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.