Learning User Information Interests Through the Extraction of Semantically Significant Phrases

Machine Learning in Information Access

Volume

Papers from the 1996 AAAI Spring Symposium

Contents

InformationFinder is an intelligent agent that learns user information interests from sets of messages or other on-line documents that users have classified. While this problem has been addressed by a number of recent research initiatives, hiformationFinder’s approach is innovative in a number of ways. First, the agent uses heuristics to extract significant phrases from documents for learning rather than use standard mathematical techniques. This enables it to learn highly general search criteria based on a small number of sample documents. Second, the agent learns standard decision trees for each user category. These decision trees are easily transformed into search query strings for standard search systems rather than requiring specialized search engines.