Enhanced Algorithmic Job Matching based on a Comprehensive Candidate Profile using NLP and Machine Learning

Pendyala, Vishnu S.; Atrey, Nishtha; Aggarwal, Tina; Goyal, Saumya

doi:10.1109/bigdataservice55688.2022.00040

Cited by 6 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Wang et al [42] propose to integrate the knowledge graph information into the sentence vector representations to fully explore the relationship between candidates' text descriptors, where the subject-term information and knowledge graph information are respectively embedded. While Pendyala et al [43] attempt to aid the hiring process through automatic profling of the candidate's social media. Furthermore, Yang et al [44] pay attention to explicitly modeling the two-way selection intentions of the job providers and the job seekers, which difers from the previous unidirectional process or overall matching.…”

Section: Job-resume Matchingmentioning

confidence: 99%

Exploring Internal and External Interactions for Semi-Structured Multivariate Attributes in Job-Resume Matching

Shao

Song

Zheng

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Job-resume matching (JRM) is the core of online recruitment services for predicting the matching degree between a job post and a resume. Most of the existing methods for JRM achieve a promising performance by simplifying this task as a matching between the free-text attributes in the job post and the resume. However, they neglect the contributions of the semistructured multivariate attributes such as education and salary, which will result in an unsuccessful prediction. To address this issue, we propose a novel approach to comprehensively explore the Internal and EXternal InTeractions for semistructured multivariate attributes in JRM, i.e., InEXIT. In detail, we first encode the key and the value of each attribute as well as its source into the same semantic space. Next, to explore the complex relationships among the multivariate attributes, we propose to hierarchically model the internal interactions among the multivariate attributes inside the job post and the resume, as well as the external interactions between the job post and the resume. In particular, a stepwise fusion mechanism is designed to respectively integrate the key embeddings and the source embeddings into the value embeddings so as to clearly indicate the key and the source of the value. Finally, we employ an aggregation matching layer to predict the matching degree. We quantify the improvements of InEXIT against the competitive baselines on a real-world dataset, showing a general improvement of 4.28%, 4.10%, and 3.56% over the state-of-the-art baseline in terms of AUC, accuracy, and F1 score, respectively.

show abstract

Section: Job-resume Matchingmentioning

confidence: 99%