Skip to Main content Skip to Navigation
Conference papers

Predicting the Best System Parameter Configuration: the (Per Parameter Learning) PPL method

Abstract : Search engines aim at delivering the most relevant information whatever the query is. To proceed, search engines employ various modules (indexing, matching, ranking), each of these modules having different variants (e.g. different stemmers, different retrieval models or weighting functions). The international evaluation campaigns in information retrieval such as TREC revealed system variability which makes it impossible to find a single system that would be the best for any of the queries. While some approaches aim at optimizing the system parameters to improve the system e ectiveness in average over a set of queries, in this paper we consider a different approach that aims at optimizing the system configuration on a per-query basis. Our method learns the configuration models in a training phase and then explores the system feature space and decides what should be the system configuration for any new query. The experimental results draw significant conclusions: (i) Predicting the best value for each system feature separately is more effective than predicting the best predefined system configuration; (ii) the method predicts successfully the optimal or most optimal system configurations for unseen queries; (iii) the mean average precision (MAP) of the system configurations predicted by our approach is much higher than the MAP of the best unique system.
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01873773
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Thursday, September 13, 2018 - 3:44:23 PM
Last modification on : Tuesday, September 8, 2020 - 10:42:05 AM

File

mothe_19043.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01873773, version 1
  • OATAO : 19043

Citation

Josiane Mothe, Mahdi Washha. Predicting the Best System Parameter Configuration: the (Per Parameter Learning) PPL method. 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2017), Sep 2017, Marseille, France. pp. 1308-1317. ⟨hal-01873773⟩

Share

Metrics

Record views

101

Files downloads

232