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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Business location recommendation using check-in data

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    13101285,14201009,12301043,13101276,16101322_CSE.pdf (903.0Kb)
    Date
    2017-08
    Publisher
    BRAC University
    Author
    Ahmed, Nasim Uddin
    Mahmud, Shafayet
    Islam, Md. Tawabul
    Shoumik, Shadman
    Habib, Muhaimin
    Metadata
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    URI
    http://hdl.handle.net/10361/8863
    Abstract
    In this era of Social Networks, large amount of data is generated from the users of social media and mobile applications on day to day basis. This data can be useful for the companies as they provide insight into the location oriented decisions of the businesses and on user behavior patterns in their regular activities. In this thesis work we are interested in the LBSN (Location Based Social network) data which is generated when the Users of social network Interact in the Online Social Networking Platforms and mobile applications by sharing their location data through “check ins” in the various Business locations. This spatial aspect of the LSBN data almost represent an online model of the physical world which can be analyzed to find key insights regarding the business locations. We have used the Geographical and Social distances to partition the city into neighborhoods as place for a new business opportunity. In technique we have used the collaborative neighborhood filtering based on similarity of neighborhoods by establishing correlation between business venues and check in patterns. We have used the New York foursquare data for our experimentation, this experimentation shows promising results for prediction of future business location.
    Keywords
    Check-in data; Location recommendation; Business location; LBSN
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 22-23).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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