Experience with Rule Induction and k-Nearest Neighbour Methods for Interface Agents that Learn
Experience with Rule Induction and k-Nearest Neighbour Methods for Interface Agents that Learn
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.
329-335
Payne, Terry R.
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Edwards, Peter
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Green, Claire L.
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Rammamoorthy, C.V.
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Wah, Benjamin
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Bastani, Farokh B.
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Spooner, David
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1997
Payne, Terry R.
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Edwards, Peter
5ee73a94-75a0-426f-ab1b-ce918b06a1ea
Green, Claire L.
7cc42294-9c74-4bee-9194-2094ba0a73f9
Rammamoorthy, C.V.
ad16db48-9a77-4938-ac5f-4288f4380cf6
Wah, Benjamin
4421bee1-d326-40b4-9e9f-bbe63cb368ab
Bastani, Farokh B.
bc4bb477-87bb-424b-8942-7e488b11cf82
Spooner, David
ad7acab7-5347-478b-8526-b6e52a34fcf7
Payne, Terry R., Edwards, Peter and Green, Claire L.
,
Rammamoorthy, C.V., Wah, Benjamin, Bastani, Farokh B. and Spooner, David
(eds.)
(1997)
Experience with Rule Induction and k-Nearest Neighbour Methods for Interface Agents that Learn.
IEEE Transactions on Knowledge and Data Engineering, 9 (2), .
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.
More information
Published date: 1997
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 257258
URI: http://eprints.soton.ac.uk/id/eprint/257258
ISSN: 1041-4347
PURE UUID: e84a4ad5-19ff-4efd-bac4-f34799d77110
Catalogue record
Date deposited: 29 Jan 2003
Last modified: 14 Mar 2024 05:55
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Contributors
Author:
Terry R. Payne
Author:
Peter Edwards
Author:
Claire L. Green
Editor:
C.V. Rammamoorthy
Editor:
Benjamin Wah
Editor:
Farokh B. Bastani
Editor:
David Spooner
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