Experience with rule induction and k-nearest neighbor methods for interface agents that learn

Author

Payne, Terry R. ; Edwards, Peter ; Green, C.L.

Author_Institution

Dept. of Comput. Sci., Aberdeen Univ., UK

Volume

9

Issue

2

fYear

1997

Firstpage

329

Lastpage

335

Abstract

Interface agents are being developed to assist users with a variety of tasks. To perform effectively, such agents need knowledge of user preferences. An agent architecture has been developed which observes a user performing tasks, and identifies features which can be used as training data by a learning algorithm. Using the learned profile, an agent can give advice to the user on dealing with new situations. The architecture has been applied to two different information filtering domains: classifying incoming mail messages (Magi) and identifying interesting USENet news articles (UNA). This paper describes the architecture and examines the results of experimentation with different learning algorithms and different feature extraction strategies within these domains

Keywords

Internet; electronic mail; feature extraction; genetic algorithms; graphical user interfaces; information retrieval systems; learning (artificial intelligence); online front-ends; software agents; user modelling; agent architecture; feature extraction strategies; information filtering; information filtering domains; instance-based learning; intelligent e-mail filter; interesting USENet news articles; interface agents; k-nearest neighbor methods; learned profile; learning algorithm; learning algorithms; mail messages; rule induction; training data; user preferences; Electronic mail; Feature extraction; Feedback; Information filtering; Information filters; Intelligent agent; Knowledge engineering; Machine learning; Postal services; Training data;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.591456

Filename

591456