An adaptive system for online document filtering

The rapid growth of the Web makes it urgent for efficient instant online document filtering. Compared to the traditional batch filtering, the new adaptive filtering technology requires less training, and can automatically improve filtering precision in filtering period. Therefore, it now becomes an effective way for Web-based document filtering. In this paper we propose a new adaptive system for online document filtering. In this system, two different scoring/weighting mechanisms, and the corresponding feedback algorithms, are implemented respectively. Based on them, an incremental profile training mechanism and an improved profile self-learning algorithm are developed. The official evaluation in the Reuters online news show the system performs better than other systems both in profile training and overall results.