- Balancing exploration and exploitation in learning to rank online
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has already been learned to produce results that are acceptable to users. We formulate this challenge as an exploration-exploitation dilemma and present the first online learning to rank algorithm that works with implicit feedback and balances exploration and exploitation. We leverage existing learning to rank data sets and recently developed click models to evaluate the proposed algorithm. Our results show that finding a balance between exploration and exploitation can substantially improve online retrieval performance, bringing us one step closer to making online learning to rank work in practice.
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- Proceedings title: Advances in information retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland,
April 18-21, 2011: proceedings
Place of publication: Berlin
Editors: P. Clough, C. Foley, C. Gurrin, G.J.F. Jones, W. Kraaij, H. Lee, V. Murdoch
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