Abstract
Recommender systems represent valuable marketing tools in e-commerce. No wonder that this field has enjoyed quite some attention from the scientific community that seeks to develop algorithms that optimize the performance of recommender systems based on the analysis of historical data. In the research reported here, we experimented with a mechanism that combines traditional collaborative filtering approaches with explicit rating. This paper describes the technique and illustrates its behavior in a test-bed we created from real-world data. The results are promising.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Sixth IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007 |
| Pages | 226-231 |
| Number of pages | 6 |
| State | Published - Dec 1 2007 |
| Event | 6th IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007 - Banff, AB, Canada Duration: Jul 2 2007 → Jul 4 2007 |
Other
| Other | 6th IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007 |
|---|---|
| Country | Canada |
| City | Banff, AB |
| Period | 7/2/07 → 7/4/07 |
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Keywords
- Collaborative filtering
- Internet tools
- Recommender systems
- User profile
ASJC Scopus subject areas
- Computer Networks and Communications
- Software
Cite this
A fully automated recommender system using collaborative filters. / Al Halabi, Wadee S.; Kubat, Miroslav; Tapia, Moiez.
Proceedings of the Sixth IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007. 2007. p. 226-231.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A fully automated recommender system using collaborative filters
AU - Al Halabi, Wadee S.
AU - Kubat, Miroslav
AU - Tapia, Moiez
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Recommender systems represent valuable marketing tools in e-commerce. No wonder that this field has enjoyed quite some attention from the scientific community that seeks to develop algorithms that optimize the performance of recommender systems based on the analysis of historical data. In the research reported here, we experimented with a mechanism that combines traditional collaborative filtering approaches with explicit rating. This paper describes the technique and illustrates its behavior in a test-bed we created from real-world data. The results are promising.
AB - Recommender systems represent valuable marketing tools in e-commerce. No wonder that this field has enjoyed quite some attention from the scientific community that seeks to develop algorithms that optimize the performance of recommender systems based on the analysis of historical data. In the research reported here, we experimented with a mechanism that combines traditional collaborative filtering approaches with explicit rating. This paper describes the technique and illustrates its behavior in a test-bed we created from real-world data. The results are promising.
KW - Collaborative filtering
KW - Internet tools
KW - Recommender systems
KW - User profile
UR - http://www.scopus.com/inward/record.url?scp=38349103377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38349103377&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780889866744
SP - 226
EP - 231
BT - Proceedings of the Sixth IASTED International Conference on Communications, Internet, and Information Technology, CIIT 2007
ER -