Maintaining dynamic channel profiles on the web

This work addresses a novel problem of maintaining channel profiles on the Web. Such channel maintenance is essential for next generation of Web 2.0 applications that provide sophisticated search and discovery services over Web information channels. Maintaining a fresh channel profile is extremely difficult due to the the dynamic nature of the channel, especially under the constraint of a limited monitoring budget. We propose a novel monitoring scheme that learns the channels' monitoring rates. The monitoring scheme is further extended to consider the content that is published on the channels. We describe a novelty detection filter that refines the monitoring rate according to the expected rate of novel content published on the channels. We further show how inter-channel profile similarities can be utilized to refine the channel monitoring rates. Using real-world data of Web feeds we study the performance of the monitoring scheme. We experiment with several monitoring policies over a large set of Web feeds and show that a policy based on learning the monitoring rate of the channels, combined with novelty detection, outperforms alternative channel monitoring policies. Our results show that the suggested content-based policy is able to maintain high quality channel profiles under limited monitoring resources.