ADAPTIVE HIGHLIGHTING OF LINKS TO ASSIST SURFING ON THE INTERNET

Zsolt Palotai, Bálint Gábor and András Lőrincz ()
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Zsolt Palotai: Department of Information Systems, Pázmány Péter sétány 1/C, Budapest, Hungary H-1117, Hungary;
Bálint Gábor: Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, Hungary H-1117, Hungary
András Lőrincz: Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, Hungary H-1117, Hungary

International Journal of Information Technology & Decision Making (IJITDM), 2005, vol. 04, issue 01, 117-139

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 AI-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.

Keywords: Internet surfing; text mining; reinforcement learning; neural network; user assistance (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:04:y:2005:i:01:n:s0219622005001416

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DOI: 10.1142/S0219622005001416

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