Abstract
Finding the most relevant jobs for job-seeker is one of the ongoing challenges in the area of e-recruitment. Text mining, information retrieval and natural language processing are some of the key concepts for matching a number of profiles and find the most textually similar job profiles given the job-seeker profile. The mission of finding the relevant jobs that are textually similar to the job-seeker profile depends mainly on the quality of three mains phases. The first early phase is basically constructing the profiles that should be involved in the matching phase through gathering data from different sources and representing this data into a form of important keywords. The second phase is the matching phase which works on mining and analyzing the text of these constructed profiles to find the similar profiles. The final phase is ranking these produced similar profiles by relevance. We introduce our intelligent similarity computing engine which works on predicting the personalized jobs for each job-seeker. It aims to empower the recommendation effectiveness in the area of e-recruitment by applying the three aforementioned phases on the problem of job recommendation. Its purpose is to recommend the textually relevant jobs based on computing the textual similarity distance between jobs and job-seekers’ profiles.
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Heggo, I.A., Abdelbaki, N. (2021). Textual Matching Framework for Measuring Similarity Between Profiles in E-recruitment. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_21
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DOI: https://doi.org/10.1007/978-3-030-72588-4_21
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