Information filtering via hill climbing, wordnet, and index patterns

The recent explosion in Internet growth has left many users awash in a sea of information, and has spurred the need for intelligent filtering systems. This paper describes work implemented in the INFOS (Intelligent News Filtering Organizational System) filtering system that is designed to reduce the user's search burden by automatically categorizing data as relevant or irrelevant based upon user interests. These predictions are learned automatically based upon features taken from input data articles and collaborative features derived from other users. The actual filtering is performed via a hybrid technique that combines a keyword-based hill climbing method, the knowledge-based conceptual representation of WordNet, and partial parsing via index patterns. A hybrid system integrating all approaches combines the benefits of each while maintaining robustness and scalability. INFOS has been tested upon Usenet news articles and preliminary tests have been performed on WWW pages.