Modeling user actions in job search

Alfan Farizki Wicaksono; Alistair Moffat; Justin Zobel

doi:10.1007/978-3-030-15712-8_42

Modeling user actions in job search

Alfan Farizki Wicaksono, Alistair Moffat, Justin Zobel

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Users of online job search tools interact with result pages in three different ways: via impressions, via clicks, and via applications. We investigate the relationship between these three kinds of interaction using logs provided by Seek.com, an Australian-based job search service. Our focus is on understanding the extent to which the three interaction types can be used to predict each other. In particular we examine models for inferring impressions from clicks, thereby providing system designers with new options for evaluating search result pages.

Original language	English
Title of host publication	Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings
Editors	Norbert Fuhr, Leif Azzopardi, Benno Stein, Claudia Hauff, Philipp Mayr, Djoerd Hiemstra
Publisher	Springer Verlag
Pages	652-664
Number of pages	13
ISBN (Print)	9783030157111
DOIs	https://doi.org/10.1007/978-3-030-15712-8_42
Publication status	Published - 1 Jan 2019
Event	41st European Conference on Information Retrieval, ECIR 2019 - Cologne, Germany Duration: 14 Apr 2019 → 18 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11437 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	41st European Conference on Information Retrieval, ECIR 2019
Country/Territory	Germany
City	Cologne
Period	14/04/19 → 18/04/19

Access to Document

10.1007/978-3-030-15712-8_42

Cite this

Wicaksono, A. F., Moffat, A., & Zobel, J. (2019). Modeling user actions in job search. In N. Fuhr, L. Azzopardi, B. Stein, C. Hauff, P. Mayr, & D. Hiemstra (Eds.), Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings (pp. 652-664). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11437 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_42

Wicaksono, Alfan Farizki ; Moffat, Alistair ; Zobel, Justin. / Modeling user actions in job search. Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. editor / Norbert Fuhr ; Leif Azzopardi ; Benno Stein ; Claudia Hauff ; Philipp Mayr ; Djoerd Hiemstra. Springer Verlag, 2019. pp. 652-664 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Wicaksono, AF, Moffat, A & Zobel, J 2019, Modeling user actions in job search. in N Fuhr, L Azzopardi, B Stein, C Hauff, P Mayr & D Hiemstra (eds), Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11437 LNCS, Springer Verlag, pp. 652-664, 41st European Conference on Information Retrieval, ECIR 2019, Cologne, Germany, 14/04/19. https://doi.org/10.1007/978-3-030-15712-8_42

Modeling user actions in job search. / Wicaksono, Alfan Farizki; Moffat, Alistair; Zobel, Justin.
Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. ed. / Norbert Fuhr; Leif Azzopardi; Benno Stein; Claudia Hauff; Philipp Mayr; Djoerd Hiemstra. Springer Verlag, 2019. p. 652-664 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11437 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Users of online job search tools interact with result pages in three different ways: via impressions, via clicks, and via applications. We investigate the relationship between these three kinds of interaction using logs provided by Seek.com, an Australian-based job search service. Our focus is on understanding the extent to which the three interaction types can be used to predict each other. In particular we examine models for inferring impressions from clicks, thereby providing system designers with new options for evaluating search result pages.

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Wicaksono AF, Moffat A, Zobel J. Modeling user actions in job search. In Fuhr N, Azzopardi L, Stein B, Hauff C, Mayr P, Hiemstra D, editors, Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings. Springer Verlag. 2019. p. 652-664. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-15712-8_42

Modeling user actions in job search

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