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    Presentation Presentation Presentation Transcript

    • Recommending Best Locations for New Restaurants --IS Seminar Topic Analysis Yingjie Zhang 1
    • 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 2
    • 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) 3
    • Model and Methods 4
    • Model and Methods Final prediction model 5
    • Data • Online reservation system • Reservation availability information • Restaurant specific information • Location-based service & Social media 6
    • Challenges • Choice of basic economic/behavior model • Modification of the basic economic model (or feature combination) • Classification for the purpose of causal effect examination 7
    • Potential Implication • Help business managers decide a new location • Help policy makers understand local economy • Help location-based service to improve their performance 8
    • 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. 9
    • •Thanks! •Q&A 10