Abstract
We describe a method for matching résumés to job descriptions provided by employers, and evaluate it on real data from a Canadian company specialized in e-recruitment. We model the task as a classifying each résumé as suitable or not for a follow up interview. We evaluate the methods on two datasets with approximately 1,500 real job descriptions and approximately 70,000 résumés, from two important industry sectors, considering several models individually and also stacked. Our stacked model shows high accuracy (often above 0.8) and consistently outperforms standard methods, including neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Büttcher, S., Clarke, C.L., Cormack, G.V.: Information Retrieval: Implementing and Evaluating Search Engines. The MIT Press, Cambridge (2016)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS. Springer, New York (2001). https://doi.org/10.1007/978-0-387-21606-5
Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: ACM SIGIR Forum, vol. 51, pp. 251–259. ACM (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lee, I.: An architecture for a next-generation holistic e-recruiting system. Commun. ACM 50(7), 81–85 (2007)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Robertson, S., Zaragoza, H., et al.: The probabilistic relevance framework: BM25 and beyond. Found. Trends\({\textregistered }\) Inf. Retrieval 3(4), 333–389 (2009)
Singh, A., Rose, C., Visweswariah, K., Chenthamarakshan, V., Kambhatla, N.: PROSPECT: A system for screening candidates for recruitment. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 659–668. ACM, New York (2010). https://doi.org/10.1145/1871437.1871523
Siting, Z., Wenxing, H., Ning, Z., Fan, Y.: Job recommender systems: a survey. In: 7th International Conference on Computer Science & Education (ICCSE) 2012, pp. 920–924. IEEE (2012)
Thompson, L.F., Braddy, P.W., Wuensch, K.L.: E-recruitment and the benefits of organizational web appeal. Comput. Hum. Behav. 24(5), 2384–2398 (2008)
Yi, X., Allan, J., Croft, W.B.: Matching resumes and jobs based on relevance models. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 809–810. ACM (2007)
Acknowledgments
This work was supported in part by an ENGAGE grant by the Natural Science and Engineering Research Council of Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Xu, P., Barbosa, D. (2018). Matching Résumés to Job Descriptions with Stacked Models. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-89656-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-89655-7
Online ISBN: 978-3-319-89656-4
eBook Packages: Computer ScienceComputer Science (R0)