Name
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358123 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
genetic algorithms, stochastic simulations, test collections | Data mining,Information retrieval,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Roitero | 1 | 30 | 13.74 |
| Andrea Brunello | 2 | 0 | 0.34 |
| Julián Urbano | 3 | 260 | 21.63 |
| Stefano Mizzaro | 4 | 34 | 8.33 |