Leveraging Search History for Improving Person-Job Fit

Hou, Yupeng; Pan, Xingyu; Zhao, Wayne Xin; Bian, Shuqing; Song, Yang; Zhang, Tao; Wen, Ji-Rong

doi:10.1007/978-3-031-00123-9_3

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Baselines We took the following state-of-the-art methods as the baselines, including content-based methods (i.e., BPJFNN (Qin et al 2018)), collaborative filtering based methods (i.e., MF (Koren, Bell, and Volinsky 2009) and NCF (He et al 2017)), hybrid methods (i.e., PJFFF (Jiang et al 2020), SHPJF (Hou et al 2022), SGL-text (Wu et al 2021) , LightGCN-text(He et al 2020, and Light-GCN+SRC), and LLMs based method (i.e., SGPT-BE (Muennighoff 2022), SGPT-ST (Reimers and Gurevych 2019), SGPT-ST+SRC).…”

Section: Evaluation Methodology and Metricsmentioning

confidence: 99%

“…Hybrid methods combine the strengths of both behavior-based and content-based approaches. Specifically, they construct the embeddings of users and jobs based on their text content and leverage user-item interaction for job recommendation (Le et al 2019;Jiang et al 2020;Hou et al 2022). However, these methods often suffer from the low quality of users' resumes.…”

Section: Related Workmentioning

confidence: 99%

“…Job recommendation is an essential task in today's online recruitment platforms, significantly improving recruitment efficiency by accurately matching job seekers (aka users) with suitable positions. Although existing job recommendation methods (Le et al 2019;Jiang et al 2020;Hou et al 2022) have achieved considerable success in recent years, they still face significant challenges, such as the low quality of user resumes and interference from the few-shot problem (Gope and Jain 2017), hindering their practical accuracy and efficiency. For example, some users may not invest sufficient effort in crafting their resumes or lack comprehensive self-awareness, resulting in incomplete and low-quality descriptions of their skills and job preferences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Du,

Luo,

Yan

et al. 2024

AAAI

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Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

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Section: Evaluation Methodology and Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Du,

Luo,

Yan

et al. 2024

AAAI

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“…Qin et al [20,21] designed four hierarchical capability-aware attention strategies to enhance representation learning. Hou et al [22] integrated job representations into user intent embeddings using self-attention mechanisms. He et al [23] applied a multi-head self-attention mechanism to enhance feature interaction modeling.…”

Section: Person-job Fitmentioning

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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“…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

Self Cite

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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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Leveraging Search History for Improving Person-Job Fit

Cited by 9 publications

References 19 publications

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

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

Modeling Two-Way Selection Preference for Person-Job Fit

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