Adaptive highlighting of links to assist surfing on the Internet

Zsolt Palotai, Bálint Gábor, András Lörincz

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Gathering of novel information from the WWW constitutes a real challenge for artificial intelligence (AI) methods. Large search engines do not offer a, satisfactory solution, their indexing cycle is long and they may offer a. huge amount of documents. An Al-based assistant agent is studied here, which sorts the availabe links by their estimated value for the user. By using this link-list the best links could be highlighted in the browser, making the user's choices easier during surfing. The method makes use of (i) "experts", i.e. pre-trained text classifiers, forming the long-term memory of the system, (ii) relative values of experts and value estimation of documents based on recent choices of the user. Value estimation adapts fast and forms the short-term memory of the system. All experiments show that surfing based filtering can efficiently highlight 10%-20% of the documents in about five steps, or less.

Original languageEnglish
Pages (from-to)117-139
Number of pages23
JournalInternational Journal of Information Technology and Decision Making
Volume4
Issue number1
DOIs
Publication statusPublished - Mar 2005

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Keywords

  • Internet surfing
  • Neural network
  • Reinforcement learning
  • Text mining
  • User assistance

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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