Journal
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Volume 4, Issue 1, Pages 117-139Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219622005001416
Keywords
Internet surfing; text mining; reinforcement learning; neural network; user assistance
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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.
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