Abstract
Recommender Systems (RS) are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. e-Recruitment is one of the domains in which RS can contribute due to presenting a list of interesting jobs to a candidate or a list of candidates to a recruiter. This study presents an up-to-date systematic review of recommender systems applied to e-Recruitment considering only papers published from 2012 up to 2020. We searched three databases for published journal articles, conference papers and book chapters. We then evaluated these works in terms of which kinds of RS were applied for e-Recruitment, what kind of information was used in the e-Recruitment RS, and how they were assessed. A total of 896 papers were collected, out of which sixty three research works were included in the survey based on the inclusion and exclusion criteria adopted. We divided the recommender types into five categories (Content-Based Recommendation 26.98%; Collaborative Filtering 6.35%; Knowledge-Based Recommendation 12.7%; Hybrid approaches 20.63%; and Other Types 33.33%); the types of information used were divided into four categories (Social Network 38.1%; Resumés and Job Posts 42.85%; Behavior or Feedback 12.7%; and Others 6.35%), and the assessment types were categorized into four types (Expert Validation 20.83%; Machine Learning Metrics 41.67%; Challenge-specific Metrics 22.92%; and Utility measures 14.58%). Although in many cases a paper may belong to more than one category for each evaluation axis, we chose the most predominant one for our categorization. In addition, there is a clear trend for hybrid and non-traditional techniques to overcome the challenges of e-Recruitment domain.
Similar content being viewed by others
References
Abel F (2016) Recsys challenge 2016: job recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp. 425–426. https://doi.org/10.1145/2959100.2959207
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749. https://doi.org/10.1109/TKDE.2005.99
Al-Otaibi S (2014) An artificial immune system for job recommendation. In: International work conference on bio-inspired intelligence: intelligent systems for biodiversity conservation, IWOBI 2014—proceedings, pp. 37–43. https://doi.org/10.1109/IWOBI.2014.6913935
Al-Otaibi ST, Ykhlef M (2012) Job recommendation systems for enhancing e-recruitment process. In: Proceedings of the international conference on information and knowledge engineering (IKE). The steering committee of the world congress in computer science. Computer Engineering and Applied Computing, WorldComp
Al-Otaibi S, Ykhlef M (2016) New artificial immune system approach based on monoclonal principle for job recommendation. Int J Adv Comput Sci Appl. https://doi.org/10.14569/ijacsa.2016.070415
Al-Otaibi S, Ykhlef M (2017) Hybrid immunizing solution for job recommender system. Front Comput Sci 11(3):511–527. https://doi.org/10.1007/s11704-016-5241-z
Al-Otaibi ST (2012) A survey of job recommender systems. Int J Phys Sci. https://doi.org/10.5897/ijps12.482
Alghieth MAA (2019) A map-based job recommender model. Int J Adv Comput Sci Appl. https://doi.org/10.14569/ijacsa.2019.0100945
Almalis N (2016) Fodra—a new content-based job recommendation algorithm for job seeking and recruiting. In: 6th international conference on information, intelligence, systems and applications. https://doi.org/10.1109/IISA.2015.7388018
Almalis ND, Tsihrintzis GA, Kyritsis E (2018) A constraint-based job recommender system integrating fodra. Int J Comput Intell Stud 7(2):103–103. https://doi.org/10.1504/ijcistudies.2018.10016063
Benabderrahmane S (2018) When deep neural networks meet job offers recommendation. In: Proceedings—international conference on tools with artificial intelligence, ICTAI 2017, pp 223–230. https://doi.org/10.1109/ICTAI.2017.00044
Bendale H, Hingoliwala HA (2015) Survey paper on website recommendation system using browser history and domain knowledge. Int J Sci Res 4(12):659–661. https://doi.org/10.21275/v4i12.nov151843
Burke R (2004) Hybrid recommender systems with case-based components. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) vol 3155, pp 91–105. https://doi.org/10.1007/978-3-540-28631-8_8
Chen W (2017) Hybrid deep collaborative filtering for job recommendation. In: 2nd IEEE international conference on computational intelligence and applications. ICCIA, pp 275–280. https://doi.org/10.1109/CIAPP.2017.8167222
Chen W (2018) Tree-based contextual learning for online job or candidate recommendation with big data support in professional social networks. IEEE Access 6:77725–77739. https://doi.org/10.1109/ACCESS.2018.2883953
Chenni O, Bouda Y, Benachour H, Zakaria C (2015) A content-based recommendation approach using semantic user profile in e-recruitment. TPNC 2015: proceedings of the fourth international conference on theory and practice of natural computing . https://doi.org/10.1007/978-3-319-26841-5_2
Dave V (2018) A combined representation learning approach for better job and skill recommendation. In: International conference on information and knowledge management, proceedings, pp 1997–2006. https://doi.org/10.1145/3269206.3272023
Desai V, Bahl D, Vibhandik S, Fatma I (2017) Implementation of an automated job recommendation system based on candidate profiles. Int Res J Eng Technol (IRJET) 4(5):1018–1021. www.irjet.net
Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177. https://doi.org/10.1145/963770.963776
Dhameliya J, Desai N (2019) Job recommendation system using content and collaborative filtering based techniques. Int J Soft Comput Eng 9(3):8–15. https://doi.org/10.35940/ijsce.c3266.099319
Dhameliya J, Desai N (2019) Job recommender systems: a survey. Innov Power Adv Comput Technol (i-PACT). https://doi.org/10.1109/i-pact44901.2019.8960231
Diaby M, Viennet E, Launay T (2013) Toward the next generation of recruitment tools. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining–ASONAM. https://doi.org/10.1145/2492517.2500266
Diaby M, Viennet E, Launay T (2014) Exploration of methodologies to improve job recommender systems on social networks. Soc Netw Anal Min. https://doi.org/10.1007/s13278-014-0227-z
Domeniconi G (2016) Job recommendation from semantic similarity of linkedin users’ skills. In: ICPRAM—proceedings of the 5th international conference on pattern recognition applications and methods, pp 270–277. https://doi.org/10.5220/0005702302700277
Dong S, Lei Z, Zhou P, Bian K (2017) Job and candidate recommendation with big data support: a contextual online learning approach. In: GLOBECOM—IEEE global communications conference. https://doi.org/10.1109/GLOCOM.2017.8255006
Enǎchescu M (2016) A prototype for an e-recruitment platform using semantic web technologies. Inf Econ 20(4):62–75
Faliagka E (2012) Taxonomy development and its impact on a self-learning e-recruitment system. IFIP Adv Inf Commun Technol 381:164–174. https://doi.org/10.1007/978-3-642-33409-2_18
Faliagka E (2014) On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed cv. Artif Intell Rev 42(3):515–528. https://doi.org/10.1007/s10462-013-9414-y
Faliagka E, Tsakalidis A, Tzimas G (2012) An integrated e-recruitment system for automated personality mining and applicant ranking. Internet Res 22(5):551–568. https://doi.org/10.1108/10662241211271545
Gangwar A, Sharma A, Singh D (2018) An art of review of personalized job recommendation engine. mitpublications.org
Gupta A, Garg D (2014) Applying data mining techniques in job recommender system for considering candidate job preferences. In: 2014 international conference on advances in computing, communications and informatics (ICACCI). https://doi.org/10.1109/icacci.2014.6968361
Gutiérrez F, Charleer S, Croon RD, Htun NN, Goetschalckx G, Verbert K (2019) Explaining and exploring job recommendations. In: Proceedings of the 13th ACM conference on recommender systems. https://doi.org/10.1145/3298689.3347001
Harzing A (2007) Publish or Perish. https://harzing.com/resources/publish-or-perish
Heap B, Krzywicki A, Wobcke W, Bain M, Compton P (2014) Combining career progression and profile matching in a job recommender system. In: Lecture notes in computer science, pp 396–408. https://doi.org/10.1007/978-3-319-13560-1_32
Heggo IA, Abdelbaki N (2018) Hybrid information filtering engine for personalized job recommender system. In: The international conference on advanced machine learning technologies and applications (AMLTA2018), pp 553–563. https://doi.org/10.1007/978-3-319-74690-6_54
Hong W, Zheng S, Wang H (2013) Dynamic user profile-based job recommender system. In: 2013 8th international conference on computer science and education. https://doi.org/10.1109/iccse.2013.6554164
Hossain MS, Arefin MS (2019) Development of an intelligent job recommender system for freelancers using client’s feedback classification and association rule mining techniques. J Softw. https://doi.org/10.17706/jsw.14.7.312-339
Hulbatte S (2019) Enhanced job recommendation system. Int J Res Appl Sci Eng Technol 7(5):3556–3563. https://doi.org/10.22214/ijraset.2019.5583
Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16(3):261–273. https://doi.org/10.1016/j.eij.2015.06.005
Jahrer M, Töscher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 693–701. https://doi.org/10.1145/1835804.1835893
Janusz A, Stawicki S, Drewniak M, Ciebiera K, Ślȩ ak D, Stencel K (2018) How to match jobs and candidates—a recruitment support system based on feature engineering and advanced analytics. In: communications in computer and information science, pp 503–514. https://doi.org/10.1007/978-3-319-91476-3_42
Jarrett J (2016) Using collaborative filtering to automate worker-job recommendations for crowdsourcing services. In: Proceedings—IEEE international conference on web services, pp. 641–645 . https://doi.org/10.1109/ICWS.2016.89
Jiang M (2019) User click prediction for personalized job recommendation. World Wide Web 22(1):325–345. https://doi.org/10.1007/s11280-018-0568-z
Kmail AB, Maree M, Belkhatir M (2015) Matchingsem: online recruitment system based on multiple semantic resources. In: 12th international conference on fuzzy systems and knowledge discovery (FSKD). https://doi.org/10.1109/fskd.2015.7382376
Kmail AB, Maree M, Belkhatir M, Alhashmi SM (2015) An automatic online recruitment system based on exploiting multiple semantic resources and concept-relatedness measures. In: IEEE 27th international conference on tools with artificial intelligence (ICTAI). https://doi.org/10.1109/ictai.2015.95
Lee Y, Hong J, Kim S (2016) Job recommendation in askstory: experiences, methods, and evaluation. In: Proceedings of the 31st annual ACM symposium on applied computing. https://doi.org/10.1145/2851613.2851862
Leksin V (2016) Job recommendation based on factorization machine and topic modelling. In: ACM international conference proceeding series. https://doi.org/10.1145/2987538.2987542
Leksin V (2017) Combination of content-based user profiling and local collective embeddings for job recommendation. In: CEUR workshop proceedings. https://ideas.repec.org/p/pra/mprapa/82808.html
Lian J (2017) Practical lessons for job recommendations in the cold-start scenario. In: ACM international conference proceeding series. https://doi.org/10.1145/3124791.3124794
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80. https://doi.org/10.1109/MIC.2003.1167344
Liu K, Shi X, Kumar A, Zhu L, Natarajan P (2016) Temporal learning and sequence modeling for a job recommender system. In: Proceedings of the recommender systems challenge on—RecSys challenge ’16 . https://doi.org/10.1145/2987538.2987540
Liu M, Zeng Z, Pan W, Peng X, Shan Z (2016) Hybrid one-class collaborative filtering for job recommendation. Smart Comput Commun SmartCom. https://doi.org/10.1007/978-3-319-52015-5_27
Liu R (2016) Rating prediction based job recommendation service for college students. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) vol 9790, pp 453–467. https://doi.org/10.1007/978-3-319-42092-9_35
Liu R (2017) A hierarchical similarity based job recommendation service framework for universitscopusq20005y students. Front Comput Sci 11(5):912–922. https://doi.org/10.1007/s11704-016-5570-y
Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32. https://doi.org/10.1016/j.dss.2015.03.008
Malherbe E, Cataldi M, Ballatore A (2015) Bringing order to the job market: Efficient job offer categorization in e-recruitment. In: Proceedings of the 38th international acm sigir conference on research and development in information retrieval. https://doi.org/10.1145/2766462.2776779
Malherbe E, Diaby M, Cataldi M, Viennet E, Aufaure M (2014) Field selection for job categorization and recommendation to social network users. In: IEEE/ACM international conference on advances in social networks analysis and mining. https://doi.org/10.1109/ASONAM.2014.6921646
Malinowski J, Wendt O, Keim T, Weitzel T (2006) Matching people and jobs: a bilateral recommendation approach. In: Proceedings of the annual hawaii international conference on system sciences, vol 6(8), pp 1–9 . https://doi.org/10.1109/HICSS.2006.266
Mansur F, Patel V, Patel M (2017) A review on recommender systems. In: International conference on innovations in information, embedded and communication systems (ICIIECS), vol 1. IEEE, pp. 1–6. https://doi.org/10.1109/ICIIECS.2017.8276182
Maree M, Kmail A, Belkhatir M (2019) Analysis and shortcomings of e-recruitment systems: towards a semantics-based approach addressing knowledge incompleteness and limited domain coverage. J Inf Sci. https://doi.org/10.1177/0165551518811449
Martinez-Gil J (2014) An overview of knowledge management techniques for e-recruitment. J Inf Knowl Manag. https://doi.org/10.1142/S0219649214500142
Martinez-Gil J, Freudenthaler B (2018) Recommendation of job offers using random forests and support vector machines. In: Proceedings of the of the EDBT/ICDT joint conference
Mishra SK, Reddy M (2016) A bottom-up approach to job recommendation system. In: Proceedings of the recommender systems challenge on—RecSys challenge. https://doi.org/10.1145/2987538.2987546
Mughaid A, Obeidat I, Hawashin B, AlZu’bi S, Aqel D (2019) A smart geo-location job recommender system based on social media posts. In: Sixth international conference on social networks analysis, management and security (SNAMS). https://doi.org/10.1109/snams.2019.8931854
Musale D, Nagpure M, Patil K (2016) Job recommendation system using profile matching and web-crawling. Int J Adv Sci Res Eng Trends
Nguyen Q (2016) Adaptive methods for job recommendation based on user clustering. In: NICS—Proceedings of 3rd national foundation for science and technology development conference on information and computer science, pp 165–170 (2016). https://doi.org/10.1109/NICS.2016.7725643
Nigam A, Roy A, Singh H, Waila H (2019) Job recommendation through progression of job selection. In: IEEE 6th international conference on cloud computing and intelligence systems (CCIS) pp 212–216
Ning X, Desrosiers C, Karypis G (2015) A comprehensive survey of neighborhood-based recommendation methods. Recomm Syst Handb. https://doi.org/10.1007/978-1-4899-7637-6_2
Pacuk A (2016) Recsys challenge 2016: job recommendations based on preselection of offers and gradient boosting. In: ACM international conference proceeding series. https://doi.org/10.1145/2987538.2987544
Patel P, Jardosh P (2016) Survey on item based and user based recommendation system in cloud. Int J Sci Res 5(4):1318–1321. https://doi.org/10.21275/v5i4.nov162852
Pessemier TD (2016) Scalable, high-performance algorithm for hybrid job recommendations. In: ACM international conference proceeding series. https://doi.org/10.1145/2987538.2987539
Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205–227. https://doi.org/10.1016/j.eswa.2017.12.020
Reusens M (2017) A note on explicit versus implicit information for job recommendation. Decis Support Syst 98:26–35. https://doi.org/10.1016/j.dss.2017.04.002
Rivas A, Chamoso P, González-Briones A, Casado-Vara R, Corchado JM (2019) Hybrid job offer recommender system in a social network. Expert Syst. https://doi.org/10.1111/exsy.12416
Rosoiu O, Popescu C (2016) E-recruiting platforms: features that influence the efficiency of online recruitment systems. Inf Econ 20(2):46–55
Said A, Bellogín A (2014) Comparative recommender system evaluation. In: Proceedings of the 8th ACM conference on recommender systems—RecSys’14 . https://doi.org/10.1145/2645710.2645746
Salazar O (2015) A casebased multiagent and recommendation environment to improve the recruitment process. Commun Comput Inf Sci 524:389–397. https://doi.org/10.1007/978-3-319-19033-4_34
Shah Jaimeel Sahu, L, (2014) A Survey of Various Hybrid based Recommendation Method. International Journal of Advanced Research in Computer Science and Software Engineering 3(11):868–872
Shahin A, Barzoki AS, Abdoulla K, Teimouri H (2018) Identification and ranking of competency-based recruitment system criteria: an empirical case study. Int J Learn Intell Capital 1(1):1–1. https://doi.org/10.1504/ijlic.2018.10017000
Shalaby W (2017) Help me find a job: a graph-based approach for job recommendation at scale. In: Proceedings—IEEE international conference on big data, big data 2018, pp 1544–1553. https://doi.org/10.1109/BigData.2017.8258088
Shishehchi S, Banihashem SY (2019) Jrdp: a job recommender system based on ontology for disabled people. Int J Technol Hum Interact. https://doi.org/10.4018/ijthi.2019010106
Silveira T, Zhang M, Lin X, Liu Y, Ma S (2017) How good your recommender system is? A survey on evaluations in recommendation. Int J Mach Learn Cybern 10(5):813–831. https://doi.org/10.1007/s13042-017-0762-9
Siting Z, Wenxing H, Ning Z, Fan Y (2012) Job recommender systems: a survey. In: 7th international conference on computer science and education (ICCSE). https://doi.org/10.1109/iccse.2012.6295216
Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: International conference on information and knowledge management, proceedings, pp 1441–1450. https://doi.org/10.1145/3357384.3357895
Ting T, Varathan K (2018) Job recommendation using facebook personality scores. Malays J Comput Sci. https://doi.org/10.22452/mjcs.vol31no4.5
Tran M (2017) A comparison study for job recommendation. In: Proceedings of KICS-IEEE international conference on information and communications with Samsung LTE and 5G special workshop, pp 199–204. https://doi.org/10.1109/INFOC.2017.8001667
Uttarwar S, Gambani S, Thakkar T, Mulla N (2020) Artificial intelligence based system for preliminary rounds of recruitment process. Comput Vis Bio-inspir Comput. https://doi.org/10.1007/978-3-030-37218-7_97
Verbert K, Duval E, Lindstaedt SN, Gillet D (2010) Context-aware recommender systems. https://doi.org/10.1007/978-0-387-85820-3_7
Wang P (2016) The analysis and design of the job recommendation model based on gbrt and time factors. In: IEEE international conference on knowledge engineering and applications, pp 29–35. https://doi.org/10.1109/ICKEA.2016.7802987
Wenxing H, Yiwei C, Jianwei Q, Yin H (2015) Ihr+: a mobile reciprocal job recommender system. In: 2015 10th international conference on computer science and education (ICCSE) . https://doi.org/10.1109/iccse.2015.7250296
Woźniak J (2014) On e-recruitment and four ways of using its methods. In: Proceedings of the 8th international scientific business and management. https://doi.org/10.3846/bm.2014.084
Woźniak J (2015) The use of gamification at different levels of e-recruitment. Manag Dyn Knowl Econ. https://www.ceeol.com/search/article-detail?id=596269
Xiao W, Xu X, Liang K, Mao J, Wang J (2016) Job recommendation with hawkes process: an effective solution for recsys challenge 2016. In: Proceedings of the recommender systems challenge. https://doi.org/10.1145/2987538.2987543
Yagci M (2017) A ranker ensemble for multi-objective job recommendation in an item cold start setting. In: ACM international conference proceeding series. https://doi.org/10.1145/3124791.3124798
Yang S (2017) Combining content-based and collaborative filtering for job recommendation system: a cost-sensitive statistical relational learning approach. Knowl-Based Syst 136:37–45. https://doi.org/10.1016/j.knosys.2017.08.017
Zhang C, Cheng X (2016) An ensemble method for job recommender systems. In: Proceedings of the recommender systems challenge on—RecSys challenge’16. https://doi.org/10.1145/2987538.2987545
Zhang Y (2015) A research of job recommendation system based on collaborative filtering. In: Proceedings–7th international symposium on computational intelligence and design, vol 1, pp 533–538. https://doi.org/10.1109/ISCID.2014.228
Zhou Q, Liao F, Chen C, Ge L (2019) Job recommendation algorithm for graduates based on personalized preference. CCF Trans Pervasive Comput Interact 1:260–274. https://doi.org/10.1007/s42486-019-00022-1
Acknowledgements
This research has been supported by Capes, CNPq, MackPesquisa and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Process No. 18/16899-6.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Freire, M.N., de Castro, L.N. e-Recruitment recommender systems: a systematic review. Knowl Inf Syst 63, 1–20 (2021). https://doi.org/10.1007/s10115-020-01522-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-020-01522-8