Abstract:

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. We propose 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 the trade-off between exploitation and exploration. The proposed algorithm is implemented for setting the filtering system's dissemination threshold, a major problem for adaptive filtering systems. Experiments with TREC-9 and TREC-10 filtering data demonstrate that the proposed method is effective.

Related Readings:

Employing EM in Pool-Based Active Learning for Text Classification A. McCallum, K. Nigam In Proceedings of ICML 1998 Heterogeneous Uncertainty Sampling for Supervised Learning D. Lewis, J. Catlett In Proceedings of ICML 1994 Active Learning for Parameter Estimation in Bayesian Networks. S. Tong, D. Koller In Proceedings of IJCAI 2001