Generation of Synthetic Query Auto Completion Logs

Privacy concerns can prohibit research access to large-scale commercial query logs. Here we focus on generation of a synthetic log from a publicly available dataset, suitable for evaluation of query auto completion (QAC) systems. The synthetic log contains plausible string sequences reflecting how u...

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Detalles Bibliográficos
Autores principales: Krishnan, Unni, Moffat, Alistair, Zobel, Justin, Billerbeck, Bodo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148242/
http://dx.doi.org/10.1007/978-3-030-45439-5_41
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author Krishnan, Unni
Moffat, Alistair
Zobel, Justin
Billerbeck, Bodo
author_facet Krishnan, Unni
Moffat, Alistair
Zobel, Justin
Billerbeck, Bodo
author_sort Krishnan, Unni
collection PubMed
description Privacy concerns can prohibit research access to large-scale commercial query logs. Here we focus on generation of a synthetic log from a publicly available dataset, suitable for evaluation of query auto completion (QAC) systems. The synthetic log contains plausible string sequences reflecting how users enter their queries in a QAC interface. Properties that would influence experimental outcomes are compared between a synthetic log and a real QAC log through a set of side-by-side experiments, and confirm the applicability of the generated log for benchmarking the performance of QAC methods.
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spelling pubmed-71482422020-04-13 Generation of Synthetic Query Auto Completion Logs Krishnan, Unni Moffat, Alistair Zobel, Justin Billerbeck, Bodo Advances in Information Retrieval Article Privacy concerns can prohibit research access to large-scale commercial query logs. Here we focus on generation of a synthetic log from a publicly available dataset, suitable for evaluation of query auto completion (QAC) systems. The synthetic log contains plausible string sequences reflecting how users enter their queries in a QAC interface. Properties that would influence experimental outcomes are compared between a synthetic log and a real QAC log through a set of side-by-side experiments, and confirm the applicability of the generated log for benchmarking the performance of QAC methods. 2020-03-17 /pmc/articles/PMC7148242/ http://dx.doi.org/10.1007/978-3-030-45439-5_41 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Krishnan, Unni
Moffat, Alistair
Zobel, Justin
Billerbeck, Bodo
Generation of Synthetic Query Auto Completion Logs
title Generation of Synthetic Query Auto Completion Logs
title_full Generation of Synthetic Query Auto Completion Logs
title_fullStr Generation of Synthetic Query Auto Completion Logs
title_full_unstemmed Generation of Synthetic Query Auto Completion Logs
title_short Generation of Synthetic Query Auto Completion Logs
title_sort generation of synthetic query auto completion logs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148242/
http://dx.doi.org/10.1007/978-3-030-45439-5_41
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