Name
Abstract | ||
|---|---|---|
The TREC-9 filtering track measures the ability of systems to build persistent user profiles which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive filtering, batch filtering, and routing. In adaptive filtering, the system begins with only a topic statement and a small number of positive examples, and must learn a better profile from on-line feedback. Batch filtering and routing are more traditional machine learning tasks where the system begins with a large sample of evaluated training documents. This report describes the track, presents some evaluation results, and provides a general commentary on lessons learned from this year's track. |
Year | Venue | DocType |
|---|---|---|
2000 | TREC | Conference |
Citations | PageRank | References |
19 | 2.62 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| STEPHEN ROBERTSON | 1 | 6204 | 669.07 |
| David A. Hull | 2 | 1282 | 214.27 |