Name
Abstract | ||
|---|---|---|
We explore the acquisition of user profiles by unobtrusively monitoring the browsing behaviour of users by applying supervised machine-learning techniques coupled with an ontological representation to extract user preferences. The HIPRICE recommender system is presented and an empirical evaluation of this approach is conducted. The performance of the integrated systems is measured and presented as well. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-39205-X_96 | RSFDGrC |
Keywords | Field | DocType |
multi-agent intelligent recommendation,user preference,ontological representation,user profile,hybrid model,browsing behaviour,integrated system,hiprice recommender system,supervised machine-learning technique,empirical evaluation,integrable system,recommender system | Recommender system,Ontology,Intelligent agent,Computer science,Supervised learning,Artificial intelligence,Integrated systems,Machine learning,The Internet | Conference |
Volume | ISSN | ISBN |
2639 | 0302-9743 | 3-540-14040-9 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ZhengYu Gong | 1 | 0 | 0.34 |
| Jing Shi | 2 | 0 | 0.68 |
| HangPing Qiu | 3 | 0 | 0.68 |