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Personalized Search: An Interactive and Iterative Approach

  • Author / Creator
    Wang, Haiming
  • In the face of an overwhelmingly information intensive Internet, searching has become the most important way to locate information efficiently. Current searching techniques are able to retrieve relevant data, however, personalization techniques are still needed to better identify different user requirements. This thesis proposes an interactive and iterative approach to infer a user's intentions implicitly, and adapt to changing user requirements. We gather relevance feedback from the user, and classify items in the query result set into different groups based on the feedback for each item. We rerank the original result set according to the user's interest towards each group. The group of the user's interest is ranked higher. We illustrate the approach using a personalized academic paper searching application and evaluate it with real users. The experimental results show improvements after applying our approach. The system design is extensible and potentially applicable to other search domains.

  • Subjects / Keywords
  • Graduation date
    2014-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3CM27
  • 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 Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Wong, Kenny (Computing Science)
  • Examining committee members and their departments
    • Zaiane, Osmar R. (Computing Science)
    • Stroulia, Eleni (Computing Science)