Zou, S. and Tao, G. and Wang, J. and Zhang, W. and Zhang, Dell (2018) On the equilibrium of query reformulation and document retrieval. In: UNSPECIFIED (ed.) Proceedings of the 4th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR). ACM, pp. 43-50. ISBN 9781450344906.
|
Text
dellzhang_ictir2018.pdf Download (1MB) | Preview |
Abstract
In this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelled as a two-player strategic game: one player, a query formulator, taking actions to produce the optimal query, is expected to maximize its own utility with respect to the relevance estimation of documents produced by the other player, a retrieval modeler; simultaneously, the retrieval modeler, taking actions to produce the document relevance scores, needs to optimize its likelihood from the training data with respect to the refined query produced by the query formulator. Their equilibrium or equilibria will be reached when both are the best responses to each other. We derive our equilibrium theory of IR using normal-form representations: when a standard relevance feedback algorithm is coupled with a retrieval model, they would share the same objective function and thus form a partnership game; by contrast, pseudo relevance feedback pursues a rather different objective than that of retrieval models, therefore the interaction between them would lead to a general-sum game (though implicitly collaborative). Our game-theoretical analyses not only yield useful insights into the two major aspects of IR, but also offer new practical algorithms for achieving the equilibrium state of retrieval which have been shown to bring consistent performance improvements in both text retrieval and item recommendation.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Keyword(s) / Subject(s): | relevance feedback, retrieval model, game theory |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for |
| Depositing User: | Dell Zhang |
| Date Deposited: | 16 Nov 2018 09:40 |
| Last Modified: | 09 Aug 2023 12:44 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/23961 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
