Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning

In the task of adaptive information filtering, a system receives a stream of documents but delivers only those that match a person's information need. As the system filters it also refines its knowledge about the user's information needs based on relevance feedback from the user. Delivering a document thus has two effects: i) it satisfies the user's information need immediately, and ii) it helps the system better satisfy the user in the future by improving its model of the user's information need. The traditional approach to adaptive information filtering fails to recognize and model this second effect. This paper proposes utility divergence as the measure of model quality. Unlike the model quality measures used in most active learning methods, utility divergence is represented on the same scale as the filtering system's target utility function. Thus it is meaningful to combine the expected immediate utility with the model quality, and to quantitatively manage...

• Type: Conference
• Year: 2003
• Where: ICML
• Authors: Yi Zhang, Wei Xu, James P. Callan
• Keywords: Adaptive Filtering Systems, ICML 2003, Information Filtering Fails, Machine Learning, TREC-10 Filtering Data