Modeling user-generated contents: An intelligent state machine for user-centric search support

Neil Y. Yen, James J. Park*, Qun Jin, Timothy K. Shih

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Researchers tend to agree that an increasing quantity of data has caused the complexity and difficulty for information discovery, management, and reuse. An essential factor relates to the increasing channels (i.e., Internet, social media, etc.) for information sharing. Finding information, especially those meaningful or useful one, that meets ultimate goal (or task) of user becomes harder then it is used to be. In this research, issues concerning the use of user-generated contents for individual search support are investigated. In order to make efficient use of usergenerated contents, an intelligent state machine, as a hybridization of graph model (Document Graph) and petrinet model (Document Sensitive Petri-Net), is proposed. It is utilized to clarify the vague usage scenario between usergenerated contents, such as discussions, posts, etc., and to identify correlations and experiences within them. As a practical contribution, an interactive search algorithm that generates potential solutions for individual is implemented. The feasibility of this research is demonstrated by a series of experiments and empirical studies with around 350,000 user-generated contents (i.e., documents) collected from the Internet and 200 users.

Original languageEnglish
Pages (from-to)1731-1739
Number of pages9
JournalPersonal and Ubiquitous Computing
Issue number8
Publication statusPublished - 2013 Dec


  • Decision support
  • Document sensitive petri-net
  • Human-centered
  • Intelligent state machine
  • User-generated contents

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences


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