Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Mahmood, Salman; Mueller, Klaus

doi:10.1109/tvcg.2019.2895642

Cited by 8 publications

(1 citation statement)

References 36 publications

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“…Recently, Yan et al [1] and Bian et al [15] also attempt to explore the preferences of job providers and job seekers by considering their historical behaviors. However, through the literature review above, we fnd that most of the existing deep learning-based methods mainly focus on the job description and the working experience, neglecting the fact that in the real world, job posts and resumes are typically made up of the semistructured multivariate attributes (also known as features), such as education, city, and salary [16][17][18][19]. Te lack of consideration for some pivotal attributes will lead to an inaccurate matching result [5].…”

Section: Introductionmentioning

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

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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.

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