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Evaluating recommendation and search in the labor market

Evaluating recommendation and search in the labor market
Evaluating recommendation and search in the labor market

This study evaluates the most popular recommender system algorithms for use on both sides of the labor market: job recommendation and job seeker recommendation. Recent research shows the drawbacks of focusing solely on predictive power when evaluating recommender systems, which become especially prominent in job- and job seeker recommendation, where aspects such as reciprocity and item spread are two other vital performance metrics for the quality of recommendations. Besides evaluating using these extra metrics, we compare recommendation with search using free text search engines. We measure what is gained, and what is lost when consuming items (jobs and job seekers) retrieved using search versus items presented via a recommender system. Based on insights in date recommendation literature, we propose changes to rating matrix construction aimed at mitigating the drawbacks of recommendation in the labor market. Our results, obtained from extensive experimentation on three datasets gathered from the Flemish public employment services, show that popular recommender algorithms perform significantly worse than user search in terms of reciprocity. Furthermore, we show that by swapping the rating matrices between two sides of a reciprocal recommender context, we can outperform user search in terms of reciprocity with limited trade off in predictive power. The insights from this research can help actors in the labor market to better understand the positioning of recommendation versus search, and to provide better job recommendations and job seeker recommendations.

Information retrieval, Job recommendation, Job seeker recommendation, Reciprocal recommendation, Recommender systems
0950-7051
62-69
Reusens, Michael
4264e5fa-ed9c-4446-ae74-a4248ae94a49
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Sels, Luc
f6cd0c72-25f4-4c47-968c-6753ba5fe5ae
Reusens, Michael
4264e5fa-ed9c-4446-ae74-a4248ae94a49
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Sels, Luc
f6cd0c72-25f4-4c47-968c-6753ba5fe5ae

Reusens, Michael, Lemahieu, Wilfried, Baesens, Bart and Sels, Luc (2018) Evaluating recommendation and search in the labor market. Knowledge-Based Systems, 152, 62-69. (doi:10.1016/j.knosys.2018.04.007).

Record type: Article

Abstract

This study evaluates the most popular recommender system algorithms for use on both sides of the labor market: job recommendation and job seeker recommendation. Recent research shows the drawbacks of focusing solely on predictive power when evaluating recommender systems, which become especially prominent in job- and job seeker recommendation, where aspects such as reciprocity and item spread are two other vital performance metrics for the quality of recommendations. Besides evaluating using these extra metrics, we compare recommendation with search using free text search engines. We measure what is gained, and what is lost when consuming items (jobs and job seekers) retrieved using search versus items presented via a recommender system. Based on insights in date recommendation literature, we propose changes to rating matrix construction aimed at mitigating the drawbacks of recommendation in the labor market. Our results, obtained from extensive experimentation on three datasets gathered from the Flemish public employment services, show that popular recommender algorithms perform significantly worse than user search in terms of reciprocity. Furthermore, we show that by swapping the rating matrices between two sides of a reciprocal recommender context, we can outperform user search in terms of reciprocity with limited trade off in predictive power. The insights from this research can help actors in the labor market to better understand the positioning of recommendation versus search, and to provide better job recommendations and job seeker recommendations.

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More information

Accepted/In Press date: 3 April 2018
e-pub ahead of print date: 5 April 2018
Published date: 15 July 2018
Keywords: Information retrieval, Job recommendation, Job seeker recommendation, Reciprocal recommendation, Recommender systems

Identifiers

Local EPrints ID: 422495
URI: http://eprints.soton.ac.uk/id/eprint/422495
ISSN: 0950-7051
PURE UUID: 843d3a2b-55c8-4005-a4d4-796cafc384f9
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 24 Jul 2018 16:31
Last modified: 18 Mar 2024 02:59

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Contributors

Author: Michael Reusens
Author: Wilfried Lemahieu
Author: Bart Baesens ORCID iD
Author: Luc Sels

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