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TRANSCRIPT
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Recommending Best Locations for New Restaurants
--IS Seminar Topic Analysis
Yingjie Zhang
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Introduction
• Location-based Data Network (LBSN)• Restaurants performance prediction
• Research Questions:• Extract and combine different geographical or mobility
features.• Detect causal effects of location-based features on restaurant
performance
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Literature Review
• Restaurant performances prediction• Location-based data usage• Features extraction (2 types)• Features combination (machine-learning-based techniques)• Data source (Foursquare check-ins dataset)
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Model and Methods
• Features:• Static geographical features: • Dynamic consumer mobility features:• Restaurants specific features:
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Model and Methods
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• Step 1 Classification• Using to cluster locations with similar characteristics• Prepare for causal effects examinations
• Step 2 Prediction
Basic economic/behavior model using
Final prediction
model
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Data
• Online reservation system• Reservation availability information• Restaurant specific information
• Location-based service & Social media
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Challenges
• Choice of basic economic/behavior model• Modification of the basic economic model (or feature
combination)• Classification for the purpose of causal effect
examination
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Potential Implication
• Help business managers decide a new location• Help policy makers understand local economy• Help location-based service to improve their
performance
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Reference
• [1] Anderson, Michael, and Jeremy Magruder. "Learning from the crowd: Regression discontinuity estimates of the effects of an online review database*." The Economic Journal 122.563 (2012): 957-989.
• [2] Noulas, Anastasios, et al. "Mining user mobility features for next place prediction in location-based services." Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 2012.
• [3] Karamshuk, Dmytro, et al. "Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement." arXiv preprint arXiv:1306.1704(2013).
• [4] Roick, Oliver, and Susanne Heuser. "Location Based Social Networks–Definition, Current State of the Art and Research Agenda." Transactions in GIS(2013).
• [5] Noulas, Anastasios, et al. "An Empirical Study of Geographic User Activity Patterns in Foursquare." ICWSM 11 (2011): 70-573.
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•Thanks!
•Q&A