Market-Aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning

Fu, Bin; Liu, Hongzhi; Zhao, Hui; Zhu, Yao; Song, Yang; Zhang, Tao; Wu, Zhonghai

doi:10.1007/978-3-031-00126-0_54

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Natural language sentences consist of words and phrases that follow grammatical rules and convey complex semantic information, involving both sequential and hierarchical elements that are crucial for understanding them. Existing research on person-job fit predominantly employs the Word2Vec method for sentence representations [27][28][29][30][31] but overlooked word positional information within sentences. Furthermore, some studies employ Doc2Vec to capture paragraph semantics and syntax but encounter quality issues due to limited data [32].…”

Section: Text Representation Learningmentioning

confidence: 99%

Your Profile Reveals Your Traits in Talent Market: An Enhanced Person-Job Fit Representation Learning

Jian,

Rao,

2024

Preprint

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Person-job fit lies at the heart of online recruitment, measuring the compatibility between job seekers and vacancies. Current researchers mainly focus on job-resume matching between job requirements and work experiences, presenting two notable limitations. Free text representation is constrained by word-level polysemy and sentence-level anisotropy. Also, overreliance on final representations hampers the exploration of inter-feature relationships. Towards this end, we proposed a novel Attentive Person-Job Fit Multifaceted feature Fusion model (APJFMF), aiming at obtaining more precise and comprehensive interactive person-job fit feature representations. The main contributions are: (1) Introduction of a method for multifaceted feature extraction and fusion from multi-source heterogeneous person-job data; (2) Enhanced the person-job free text representations through unsupervised fine-tuning of BERT; (3) Investigated global feature interactions by integrating diverse attention mechanisms. Extensive experiments on a real-world recruitment dataset confirmed the effectiveness of APJFMF and its individual components. The code is released at https://github.com/raochongzhi/APJFMF.

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Section: Text Representation Learningmentioning

confidence: 99%

Your Profile Reveals Your Traits in Talent Market: An Enhanced Person-Job Fit Representation Learning

Jian,

Rao,

2024

Preprint

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“…Fu [9] proposed a bilateral multi-behavior sequence model to describe the dynamic comprehensive preferences of users. Besides, some studies have explored other related behaviors of users to improve person-job fit, such as search history [13] and interactive feedbacks [8]. However, the two-way selection preference, which naturally exists in online recruitment scenarios, is not explicitly modeled in these methods.…”

Section: Related Work 21 Person-job Fitmentioning

confidence: 99%

Modeling Two-Way Selection Preference for Person-Job Fit

Chen¹,

Hou²,

Song³

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dualperspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN.

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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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Market-Aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning

Cited by 3 publications

References 15 publications

Your Profile Reveals Your Traits in Talent Market: An Enhanced Person-Job Fit Representation Learning

Your Profile Reveals Your Traits in Talent Market: An Enhanced Person-Job Fit Representation Learning

Modeling Two-Way Selection Preference for Person-Job Fit

A Challenge-based Survey of E-recruitment Recommendation Systems

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