Location recommendations for new businesses using check-in data
Date
2016-12Source Title
IEEE International Conference on Data Mining Workshops, ICDMW
Publisher
IEEE
Pages
1110 - 1117
Language
English
Type
Conference PaperItem Usage Stats
243
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255
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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 systemsLocation 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