Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/102458
Title: A neural information retreival approach for resume searching in a recruitment agency
Authors: Grech, Brandon
Suda, David
Keywords: Artificial intelligence
Information storage and retrieval
Computational intelligence
Pattern recognition
Neural networks (Computer science)
Issue Date: 2020
Publisher: SCITEPRESS Digital Library
Citation: Grech, B. & Suda, D. (2020). A Neural Information Retreival Approach for Resume Searching in a Recruitment Agency. International Conference on Pattern Recognition Applications and Methods 2020, Valletta. 645-651.
Abstract: Finding resumes that match a job description can be a daunting task for a recruitment agency, due to the fact that these agencies are dealing with hundreds of job descriptions and tens of thousands of resumes simultaneously. In this paper we explain a search method devised for a recruitment agency by measuring similarity between resume documents and job description documents. Document vectors are obtained via TF-IDF weights from word embeddings arising from a neural language model with a skip-gram loss function. We show that, with this approach, successful searches can be achieved, and that the number of skips assumed in the skip gram loss function determines how successful it can be for different job descriptions.
URI: https://www.um.edu.mt/library/oar/handle/123456789/102458
Appears in Collections:Scholarly Works - FacSciSOR

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