Abstract
In this paper, we describe a new scheme to learn dynamic users' interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.
| Original language | English (US) |
|---|---|
| Title of host publication | International Conference on Information and Knowledge Management, Proceedings |
| Publisher | ACM |
| Pages | 405-412 |
| Number of pages | 8 |
| ISBN (Print) | 1581131461 |
| State | Published - 1999 |
| Event | Proceedings of the 1999 8th International Conference on Information Knowledge Management (CIKM'99) - Kansas City, MO, USA Duration: Nov 2 1999 → Nov 6 1999 |
Other
| Other | Proceedings of the 1999 8th International Conference on Information Knowledge Management (CIKM'99) |
|---|---|
| City | Kansas City, MO, USA |
| Period | 11/2/99 → 11/6/99 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)
Cite this
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Adaptive algorithm for learning changes in user interests. / Widyantoro, Dwi H.; Ioerger, Thomas R.; Yen, John.
International Conference on Information and Knowledge Management, Proceedings. ACM, 1999. p. 405-412.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Adaptive algorithm for learning changes in user interests
AU - Widyantoro, Dwi H.
AU - Ioerger, Thomas R.
AU - Yen, John
PY - 1999
Y1 - 1999
N2 - In this paper, we describe a new scheme to learn dynamic users' interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.
AB - In this paper, we describe a new scheme to learn dynamic users' interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.
UR - http://www.scopus.com/inward/record.url?scp=0033279068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033279068&partnerID=8YFLogxK
M3 - Conference contribution
SN - 1581131461
SP - 405
EP - 412
BT - International Conference on Information and Knowledge Management, Proceedings
PB - ACM
ER -