- Contextual Bandits for Information Retrieval
- Book/source title
- NIPS 2011: Proceedings of the Conference on Neural Information Processing Systems, Workshop on Bayesian Optimization, Experimental Design and Bandits: Theory and Applications
- Pages (from-to)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
In this paper we give an overview of and outlook on research at the intersection
of information retrieval (IR) and contextual bandit problems. A critical problem
in information retrieval is online learning to rank, where a search engine strives
to improve the quality of the ranked result lists it presents to users on the basis
of those users’ interactions with those result lists. Recently, researchers have
started to model interactions between users and search engines as contextual bandit
problems, and initial methods for learning in this setting have been devised.
Our research focuses on two aspects: balancing exploration and exploitation and
inferring preferences from implicit user interactions. This paper summarizes our
recent work on online learning to rank for information retrieval and points out
challenges that are characteristic of this application area.
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