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Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy.

Yi Cai1, Qing Li2, Haoran Xie2

  • 1School of Software Engineering, South China University of Technology, Guangzhou, China.

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|June 19, 2014
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Summary

This study introduces a new method for personalized search using collaborative tagging systems. It improves resource retrieval quality by modeling user and resource profiles, outperforming existing methods.

Keywords:
Big dataFolksonomyPersonalized searchSocial mediaUser profile

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Area of Science:

  • Information Science
  • Computer Science
  • Web Science

Background:

  • The proliferation of resource-sharing websites necessitates improved personalized search capabilities.
  • Collaborative tagging systems (folksonomies) offer a powerful mechanism for organizing and retrieving shared online resources.
  • Existing tag-based personalized search methods have limitations in accurately capturing user information needs.

Purpose of the Study:

  • To address the limitations of current tag-based personalized search techniques.
  • To propose a novel method for modeling user and resource profiles within collaborative tagging systems.
  • To enhance the quality and relevance of personalized resource retrieval.

Main Methods:

  • Developed a new method to model user and resource profiles using normalized term frequency to represent user tag preferences.
  • Introduced a novel search approach that treats relevance measurement as a fuzzy satisfaction problem, moving beyond simple keyword matching.
  • Implemented a prototype system, the Folksonomy-based Multimedia Retrieval System (FMRS), to test the proposed method.

Main Results:

  • The proposed method effectively models user preferences and resource characteristics in collaborative tagging environments.
  • The fuzzy satisfaction approach to relevance measurement enhances the accuracy of personalized search.
  • Experimental results demonstrate that the new method outperforms baseline approaches in resource retrieval quality.

Conclusions:

  • The novel profile modeling and fuzzy relevance measurement offer a significant advancement in personalized search within collaborative tagging systems.
  • The FMRS prototype validates the effectiveness of the proposed approach.
  • This research contributes to improving the user experience on large-scale resource-sharing platforms.