The TREC 2002 Filtering Track Report.

Abstract

The TREC-10 ltering 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.