Abstract
Due to the constant growth in online recruitment, job portals are starting to receive thousands of resumes in diverse styles and formats from job seekers who have different fields of expertise and specialize in various domains. Accordingly, automatically extracting structured information from such resumes is needed not only to support the automatic matching between candidate resumes and their corresponding job offers, but also to efficiently route them to their appropriate occupational categories to minimize the effort required for managing and organizing them. As a result, instead of searching globally in the entire space of resumes and job posts, resumes that fall under a certain occupational category are only those that will be matched to their relevant job post. In this research work, we present a hybrid approach that employs conceptual-based classification of resumes and job postings and automatically ranks candidate resumes (that fall under each category) to their corresponding job offers. In this context, we exploit an integrated knowledge base for carrying out the classification task and experimentally demonstrate - using a real-world recruitment dataset- achieving promising precision results compared to conventional machine learning based resume classification approaches.
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References
Faliagka, E., Iliadis, L., Karydis, I., Rigou, M., Sioutas, S., Tsakalidis, A., Tzimas, G.: On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV. Artif. Intell. Rev. 42(3), 515–528 (2014)
Kessler, R., Béchet, N., Roche, M., Torres-Moreno, J., El-Bèze, M.: A hybrid approach to managing job offers and candidates. Inf. Process. Manage. 48(6), 1124–1135 (2012)
Chen, J., Niu, Z., Fu, H.: A novel knowledge extraction framework for resumes based on text classifier. In: Proceedings of the International Conference on Web-Age Information Management, pp. 540–543. Springer International Publishing (2015)
Schmitt, T., Philippe C., Michele, S.: Matching jobs and resumes: a deep collaborative filtering task. In: Proceedings of the 2nd Global Conference on Artificial Intelligence, pp. 1–14 (2016)
Hauff, C., Georgios G.: Matching GitHub developer profiles to job advertisements. In: Proceedings of the 12th Working Conference on Mining Software Repositories, pp. 362–366 (2015)
Pimplikar, R., Singh, A., Varshney, R., Visweswariah, K.: Efficient multifaceted screening of job applicants. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 661–671. ACM (2013)
Kmail, A., Maree, M., Belkhatir, M., Alhashmi, S.: An automatic online recruitment system based on exploiting multiple semantic resources and concept-relatedness measures. In: Proceedings of the IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 620–627 (2015)
Kmail, A., Maree, M., Belkhatir, M.: MatchingSem: online recruitment system based on multiple semantic resources. In: Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2654–2659. IEEE (2015)
Hong, W., et al.: A job recommender system based on user clustering. J. Comput. 8(8), 1960–1967 (2013)
Kumaran, V.S., Sankar, A.: Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping EXPERT. Int. J. Metadata Semant. Ontol. 8(1), 56–64 (2013)
Kessler, R., Béchet, N., Torres-Moreno, J.M., Roche, M., El-Bèze, M.: Job offer management: how improve the ranking of candidates. In: Rauch, J. et al.(eds.) Foundations of Intelligent Systems, pp. 431–441. Springer, Heidelberg (2009)
Yu, K., Guan, G., Zhou, M.: Resume information extraction with cascaded hybrid model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 499–506. Association for Computational Linguistics (2005)
Javed, F., et al: Carotene: a job title classification system for the online recruitment domain. In: Proceedings of the IEEE First International Conference on Big Data Computing Service and Applications (BigDataService), pp. 286–293 (2015)
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, Amsterdam (2007)
Faliagka, E., et al: Application of machine learning algorithms to an online recruitment system. In: The Seventh International Conference on Internet and Web Applications and Services, ICIW 2012, pp. 215–220 (2012)
Bekkerman, R., Gavish, M.: High-precision phrase-based document classification on a modern scale. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 231–239. ACM (2011)
Kessler, M., et al: E-Gen: automatic job offer processing system for human resources. In: Proceedings of the Artificial Intelligence 6th Mexican International Conference on Advances in Artificial Intelligence, pp. 985–995. Springer, Aguascalientes (2007)
Clyde, S., Zhang, J., Yao, C.-C.: An object-oriented implementation of an adaptive classification of job openings. In: Proceedings of the 11th Conference on Artificial Intelligence for Applications, pp. 9–16. IEEE (1995)
About Occupational Information Network (O*NET). https://onet.rti.org/about.cfm. Date Visited 5 Feb 2016
Miller, G.A.: WordNet: a lexical database for English. Comm. ACM 38(11), 39–41 (1995)
Hoffart, J., et al.: YAGO2: exploring and querying world knowledge in time, space, context, and many languages. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 229–232. ACM, Hyderabad (2011)
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Zaroor, A., Maree, M., Sabha, M. (2018). A Hybrid Approach to Conceptual Classification and Ranking of Resumes and Their Corresponding Job Posts. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_10
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DOI: https://doi.org/10.1007/978-3-319-59421-7_10
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