ResumeGAN

Luo, Yong; Zhang, Huaizheng; Wen, Yonggang; Zhang, Xinwen

doi:10.1145/3357384.3357899

Cited by 24 publications

(5 citation statements)

References 24 publications

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“…(Yan et al 2019) introduced a profiling memory to learn latent preference representation by interacting with both job and resume. (Luo et al 2019) explored the effectiveness of adversarial training for job-resume matching. In addition to the aforementioned research, some researchers considered multi-granularity interactions.…”

Section: Related Work Job Recommendationmentioning

confidence: 99%

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

Wu,

Qiu,

Zheng

et al. 2024

AAAI

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Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for graph semantic mining in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including promoting out-of-distribution (OOD) applications. We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs and uncover underlying patterns and relationships. Specifically, we propose a meta-path prompt constructor that aids LLM recommender in grasping the semantics of behavior graphs for the first time and design a corresponding path augmentation module to alleviate the prompt bias introduced by path-based sequence input. By facilitating this capability, our framework enables personalized and accurate job recommendations for individual users. We evaluate the effectiveness of our approach on comprehensive real-world datasets and demonstrate its ability to improve the relevance and quality of recommended results. This research not only sheds light on the untapped potential of large language models but also provides valuable insights for developing advanced recommendation systems in the recruitment market. The findings contribute to the growing field of natural language processing and offer practical implications for enhancing job search experiences.

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Section: Related Work Job Recommendationmentioning

confidence: 99%

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

Wu,

Qiu,

Zheng

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

“…Existing works that fall in this category use directed acyclic causal graphs (Tran et al, , 2022 and knowledge representation using domain ontology (Shishehchi and Banihashem, 2019;Ntioudis et al, 2022). • Hybrid: Hybrid approaches utilize combinations of the previous approaches (Luo et al, 2019). No such hybrid could be found in the conducted literature search.…”

Section: Recommendation Algorithms In the Recruitment Domainmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…Similarly, Pajic-Bach et al ( 2020) used text mining to group job advertisements into clusters, developing a profile of Industry 4.0 jobs. Luo et al (2019) introduced a framework that calculates the matching score between a talent resume and a job post, outperforming other approaches through opposing learning.…”

Section: Related Workmentioning

confidence: 99%

A predictive analytics solution matching job seekers' talent and employers' demands based on machine learning

Rahmani,

Groot,

Rahmani

2023

Preprint

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The Covid-19 Pandemic has led to an increase in online recruitment. Many job seekers and employers post information about their talents, characteristics, and demands on job search websites where a large amount of data is cumulated in an unparsed way. Therefore, job seekers and employers cannot always find their way through this. This research used a sample of data from the most dependable employment and job search website in Iran. By examining the database provided by the website, the study aims to identify, cluster, predict and analyze data related to job seekers and the labour market. This study utilized the CRISP-DM methodology to analyze job seeker and employers' demands data, utilizing clustering algorithms such as self-organizing maps (SOM), Fuzzy C-means (FCM), K-Means, and latent Dirichlet allocation (LDA), with validation using the silhouette criterion. Then Multi-layer Perceptron (MLP), Naive Bayes, Learning Vector Quantization (LVQ), Support Vector Machine (SVM), and Language Model (LM) classification algorithms were used for prediction and classification processes. Results showed that the SOM algorithm had the best clustering with an average silhouette index of 0.92 for job seekers and 0.89 for the labour market. The MLP algorithm with SOM clustering was chosen as the best algorithm for prediction and classification processes with an accuracy of 0.83 which is a relatively high level of accuracy in predictive analytics. JEL code: J23, J21, J24, C38, C55.

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ResumeGAN

Cited by 24 publications

References 24 publications

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

Reviews in Recommender Systems: 2022

A predictive analytics solution matching job seekers' talent and employers' demands based on machine learning

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