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

  • Machine learning
  • Relevance feedback
  • Personalized search

• Graduation date

  Fall 2014

• 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.