Incorporating web analysis into neural networks: An example in hopfield net searching

Michael Chau, Hsinchun Chen

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Neural networks have been used in various applications on the World Wide Web, but most of them only rely on the available input-output examples without incorporating Web-specific knowledge, such as Web link analysis, into the network design. In this paper, we propose a new approach in which the Web is modeled as an asymmetric Hopfield Net. Each neuron in the network represents a Web page, and the connections between neurons represent the hyperlinks between Web pages. Web content analysis and Web link analysis are also incorporated into the model by adding a page content score function and a link score function into the weights of the neurons and the synapses, respectively. A simulation study was conducted to compare the proposed model with traditional Web search algorithms, namely, a breadth-first search and a best-first search using PageRank as the heuristic. The results showed that the proposed model performed more efficiently and effectively in searching for domain-specific Web pages. We believe that the model can also be useful in other Web applications such as Web page clustering and search result ranking.

Original languageEnglish (US)
Pages (from-to)352-358
Number of pages7
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume37
Issue number3
DOIs
StatePublished - May 1 2007

    Fingerprint

Keywords

  • Hopfield net
  • Neural network
  • Spreading activation
  • Web analysis
  • Web mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this