Abstract
Recently proposed methods allow the generation of simulated scores representing the values of an effectiveness metric, but they do not investigate the generation of the actual lists of retrieved documents. In this paper we address this limitation: we present an approach that exploits an evolutionary algorithm and, given a metric score, creates a simulated relevance profile (i.e., a ranked list of relevance values) that produces that score. We show how the simulated relevance profiles are realistic under various analyses.
Original language | English |
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Title of host publication | CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Publisher | Association for Computing Machinery (ACM) |
Pages | 2217-2220 |
Number of pages | 4 |
ISBN (Electronic) | 9781450369763 |
DOIs | |
Publication status | Published - 3 Nov 2019 |
Event | 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China Duration: 3 Nov 2019 → 7 Nov 2019 |
Conference
Conference | 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 |
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Country/Territory | China |
City | Beijing |
Period | 3/11/19 → 7/11/19 |
Keywords
- Genetic algorithms
- Stochastic simulations
- Test collections