SYSTEM AND METHOD FOR TOPIC-BASED DOCUMENT ANALYSIS FOR INFORMATION FILTERING

An information filtering process designed to sort through large volumes of dynamically generated textual information, incrementally learning process that learns as new text documents arrive and the user grades them by providing feedback. Text-based documents either dynamically retrieved from the Web or available in a textual repository on an Intranet are represented by applying key-word weighting's after capturing the user reasoning for classifying the document as relevant or irrelevant. When a new item (document) arrives, the learning agent suggests a classification and also provides an explanation by pointing out the main features (key-phrases) of the item (document) responsible for its classification. The user looks at this and provides hints by showing a list of features (key-phrases) and are truly responsible for a particular way of classifying the document. This interaction method contributes to the learning process. The apparatus includes a feedback-based clustering scheme that models user's interest profiles, a simple neural adaptation method for leaning the cluster centers to provide personalized information filtering for information seekers.