Matching Job Circular with Resume Using Different Natural Language Processing Based Algorithms

Chowdhury, S. M. Shawal; Chowdhury, M A Hassan; Sultana, Azmeri

doi:10.1007/978-3-031-34619-4_34

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…On the other hand, Much scienti c work has been carried out to develop methods and approaches to align CVs with job offers such as [9] [10] [11] [12] and others. This work focuses on extracting key information from CVs and job offers, as well as comparing and evaluating the correspondence between these two documents.…”

Section: Research Work Done On Text Classi Cation With Bert [4] [5] [6]mentioning

confidence: 99%

CV classification in a big data environment using BERT and alignment learning

CHAFI,

KABIL,

KAMOUSS

2023

Preprint

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Today, text classification has always been a crucial discipline in the field of text data processing. However, with the advent of Big Data, text classification has reached new heights in terms of the volume of data to be processed and the complexity of the tasks. This task is of great importance in many fields, such as spam detection, sentiment analysis, document categorization, content recommendation and many more. In this work, we focus specifically on resume classification, an area that presents unique challenges due to diverse formats, ambiguous language, and variations in applicants' work experiences. We propose an innovative approach for distributed classification of CVs using contextual alignment techniques with the job offer and the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) which constitutes a major advance in this field , and using RNN (Recurrent Neural Network) to solve text classification problems. We use a distributed processing approach to leverage the parallel computing power of Spark, enabling large volumes of data to be processed efficiently. The main objective of our study is to improve the relevance of CV classification by leveraging the pre-trained language models and distributed processing power of Spark.

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Section: Research Work Done On Text Classi Cation With Bert [4] [5] [6]mentioning

confidence: 99%

CV classification in a big data environment using BERT and alignment learning

CHAFI,

KABIL,

KAMOUSS

2023

Preprint

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A Challenge-based Survey of E-recruitment Recommendation Systems

Mashayekhi,

Li,

Kang

et al. 2024

ACM Comput. Surv.

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E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.

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Matching Job Circular with Resume Using Different Natural Language Processing Based Algorithms

Cited by 2 publications

References 2 publications

CV classification in a big data environment using BERT and alignment learning

CV classification in a big data environment using BERT and alignment learning

A Challenge-based Survey of E-recruitment Recommendation Systems

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