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 pro...
Ausführliche Beschreibung
Autor*in: |
Reusens, Michael [verfasserIn] Lemahieu, Wilfried [verfasserIn] Baesens, Bart [verfasserIn] Sels, Luc [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Knowledge-based systems - Amsterdam [u.a.] : Elsevier Science, 1987, 152, Seite 62-69 |
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Übergeordnetes Werk: |
volume:152 ; pages:62-69 |
DOI / URN: |
10.1016/j.knosys.2018.04.007 |
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Katalog-ID: |
ELV001134167 |
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245 | 1 | 0 | |a Evaluating recommendation and search in the labor market |
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520 | |a 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. | ||
650 | 4 | |a Recommender systems | |
650 | 4 | |a Reciprocal recommendation | |
650 | 4 | |a Job recommendation | |
650 | 4 | |a Job seeker recommendation | |
650 | 4 | |a Information retrieval | |
700 | 1 | |a Lemahieu, Wilfried |e verfasserin |4 aut | |
700 | 1 | |a Baesens, Bart |e verfasserin |4 aut | |
700 | 1 | |a Sels, Luc |e verfasserin |4 aut | |
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2018 |
allfields |
10.1016/j.knosys.2018.04.007 doi (DE-627)ELV001134167 (ELSEVIER)S0950-7051(18)30172-2 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Reusens, Michael verfasserin (orcid)0000-0002-9796-2016 aut Evaluating recommendation and search in the labor market 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Recommender systems Reciprocal recommendation Job recommendation Job seeker recommendation Information retrieval Lemahieu, Wilfried verfasserin aut Baesens, Bart verfasserin aut Sels, Luc verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 152, Seite 62-69 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:152 pages:62-69 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 152 62-69 |
spelling |
10.1016/j.knosys.2018.04.007 doi (DE-627)ELV001134167 (ELSEVIER)S0950-7051(18)30172-2 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Reusens, Michael verfasserin (orcid)0000-0002-9796-2016 aut Evaluating recommendation and search in the labor market 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Recommender systems Reciprocal recommendation Job recommendation Job seeker recommendation Information retrieval Lemahieu, Wilfried verfasserin aut Baesens, Bart verfasserin aut Sels, Luc verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 152, Seite 62-69 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:152 pages:62-69 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 152 62-69 |
allfields_unstemmed |
10.1016/j.knosys.2018.04.007 doi (DE-627)ELV001134167 (ELSEVIER)S0950-7051(18)30172-2 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Reusens, Michael verfasserin (orcid)0000-0002-9796-2016 aut Evaluating recommendation and search in the labor market 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Recommender systems Reciprocal recommendation Job recommendation Job seeker recommendation Information retrieval Lemahieu, Wilfried verfasserin aut Baesens, Bart verfasserin aut Sels, Luc verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 152, Seite 62-69 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:152 pages:62-69 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 152 62-69 |
allfieldsGer |
10.1016/j.knosys.2018.04.007 doi (DE-627)ELV001134167 (ELSEVIER)S0950-7051(18)30172-2 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Reusens, Michael verfasserin (orcid)0000-0002-9796-2016 aut Evaluating recommendation and search in the labor market 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Recommender systems Reciprocal recommendation Job recommendation Job seeker recommendation Information retrieval Lemahieu, Wilfried verfasserin aut Baesens, Bart verfasserin aut Sels, Luc verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 152, Seite 62-69 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:152 pages:62-69 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 152 62-69 |
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10.1016/j.knosys.2018.04.007 doi (DE-627)ELV001134167 (ELSEVIER)S0950-7051(18)30172-2 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Reusens, Michael verfasserin (orcid)0000-0002-9796-2016 aut Evaluating recommendation and search in the labor market 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Recommender systems Reciprocal recommendation Job recommendation Job seeker recommendation Information retrieval Lemahieu, Wilfried verfasserin aut Baesens, Bart verfasserin aut Sels, Luc verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 152, Seite 62-69 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:152 pages:62-69 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 152 62-69 |
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Evaluating recommendation and search in the labor market |
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Evaluating recommendation and search in the labor market |
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Reusens, Michael Lemahieu, Wilfried Baesens, Bart Sels, Luc |
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evaluating recommendation and search in the labor market |
title_auth |
Evaluating recommendation and search in the labor market |
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. |
abstractGer |
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. |
abstract_unstemmed |
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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title_short |
Evaluating recommendation and search in the labor market |
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Lemahieu, Wilfried Baesens, Bart Sels, Luc |
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up_date |
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