Exploration and exploitation in adaptive filtering based on Bayesian active learning

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Exploration and exploitation in adaptive filtering based on Bayesian active learning (2003)

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by Yi Zhang , Wei Xu , Jamie Callan

Venue:	Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003
Citations:	18 - 3 self

BibTeX

@INPROCEEDINGS{Zhang03explorationand,
    author = {Yi Zhang and Wei Xu and Jamie Callan},
    title = {Exploration and exploitation in adaptive filtering based on Bayesian active learning},
    booktitle = {Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003},
    year = {2003},
    pages = {896--903}
}

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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. 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 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. 1.

Keyphrases

bayesian active learning user information need adaptive filtering model quality utility divergence second effect filtering system active learning method person information need adaptive information filtering relevance feedback model quality measure expected immediate utility target utility function adaptive information filtering system dissemination threshold traditional approach trec-10 filtering data demonstrate major problem

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