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Utilizing Context for Novel Point of Interest Recommendation

  • Author / Creator
    Morawski, Jason M
  • Recommender systems are a modern solution for suggesting new items to users. One of their uses is for novel point of interest recommendation, recommending locations to a user which they have not visited. This can be applied to a location-based social network, which contains information about their users' travel history and social connections. Within this context, there are various challenges, such as data sparsity, that limit recommendation effectiveness. We propose an algorithm for personalized novel point of interest recommendation to overcome these challenges. Our solution leverages social, temporal, and spatial context, together with collaborative filtering and a classification algorithm.

  • Subjects / Keywords
  • Graduation date
    2017-11:Fall 2017
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3RV0DD90
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Specialization
    • Software Engineering and Intelligent Systems
  • Supervisor / co-supervisor and their department(s)
    • Miller, James (Electrical and Computer Engineering)
    • Dick, Scott (Electrical and Computer Engineering)
  • Examining committee members and their departments
    • Miller, James (Electrical and Computer Engineering)
    • Dick, Scott (Electrical and Computer Engineering)
    • Liang, Hao (Electrical and Computer Engineering)
    • Niu, Di (Electrical and Computer Engineering)