Zuccon, G. and Azzopardi, L. and Zhang, Dell and Wang, J. (2012) Top-k retrieval using facility location analysis. In: Baeza-Yates, R.A. and de Vries, A.P. and Zaragoza, H. and Cambazoglu, B.B. and Murdock, V. and Lempel, R. and Silvestri, F. (eds.) Advances in Information Retrieval. Lecture Notes In Computer Science 7224. Berlin, Germany: Springer, pp. 305-316. ISBN 9783642289965.
Abstract
The top-k retrieval problem aims to find the optimal set of k documents from a number of relevant documents given the user’s query. The key issue is to balance the relevance and diversity of the top-k search results. In this paper, we address this problem using Facility Location Analysis taken from Operations Research, where the locations of facilities are optimally chosen according to some criteria. We show how this analysis technique is a generalization of state-of-the-art retrieval models for diversification (such as the Modern Portfolio Theory for Information Retrieval), which treat the top-k search results like “obnoxious facilities” that should be dispersed as far as possible from each other. However, Facility Location Analysis suggests that the top-k search results could be treated like “desirable facilities” to be placed as close as possible to their customers. This leads to a new top-k retrieval model where the best representatives of the relevant documents are selected. In a series of experiments conducted on two TREC diversity collections, we show that significant improvements can be made over the current state-of-the-art through this alternative treatment of the top-k retrieval problem.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Administrator |
| Date Deposited: | 30 May 2013 12:48 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7099 |
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