Clustering

doi:10.1002/9781119302872.ch8

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Clustering is the process of discovering a series of trends in a set of data. It produced positive results, especially in classifiers and predictors (Homenda & Pedrycz, 2018). Entities in one cluster should be as dissimilar to entities in another cluster as possible.…”

Section: Clustering Algorithmsmentioning

confidence: 96%

Implementing AIRM: A New AI Recruiting Model for the Saudi Arabia Labour Market

Aleisa

Beloff

White

2021

Preprint

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Artificial intelligence is one of the most rapidly evolving technologies today, and it has been used in a variety of application domains. Recently, AI technology has shone in the field of recruiting. Many researchers are working on expanding its capabilities with various applications that will aid in the recruitment process. However, importantly, having an open labour market without a cohesive data centre makes it difficult to monitor, integrate, analyse, and build an evaluation matrix that helps reach the best match of a job candidate to a job vacancy. Moreover, previous research in this domain, where AI serves in the recruiting field, has focused solely on linear and non-linear models that, once trained, lack user preference personalisation. Furthermore, it was more focused on commercial applications than on government services. This paper implements the AIRM architecture and found impressive results by exploring AI and Natural Language Processing matching job candidates. We used a suitable data repository technique with three processing layers, each with a different appropriate model. First, the Initial Screening layer usesthe BIRCH clustering algorithm. The Mapping layer then performs an approximate nearest neighbour search using a Sentence transformer and ranking with a Facebook AI Similarity Search Finally, the Preferences layer takes the user's preferences as a list and sorts the results using a pretrained Cross-Encoders model that considers the weight of the more important words. We completed the implementation of AIRM and obtained some promising results. We discovered that when at least one expert agrees with the system's choice, the AIRM achieves an overall matching accuracy of 82%. In the time performance test, AIRM outperforms human performance in terms of task execution time. It completed the task in 2.4 minutes, but humans took more than three days on average, which is essential when pre-selecting a set of candidates and positions. AIRM will empower the Saudi government's immediate and strategic decisions by gaining comprehensive insight into the labour market and speeding up the recruiting process.

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Section: Clustering Algorithmsmentioning

confidence: 96%

Implementing AIRM: A New AI Recruiting Model for the Saudi Arabia Labour Market

Aleisa

Beloff

White

2021

Preprint

View full text Add to dashboard Cite

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Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market

Aleisa,

Beloff,

White

2023

J Innov Entrep

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One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product (GDP). This paper aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market by matching job seekers with vacant positions. Currently, human experts carry out this process; however, this is time-consuming and labour-intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate or analyse labour-market data, resulting in several inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour-market without a national data centre. This paper proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research collaborators who are all professionals in Human Resources (HR). As this research is data-driven in nature, it requires collaboration from domain experts. The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERT pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 min, whereas human experts took more than 6 days on average. Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data.

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Clustering

Cited by 2 publications

References 17 publications

Implementing AIRM: A New AI Recruiting Model for the Saudi Arabia Labour Market

Implementing AIRM: A New AI Recruiting Model for the Saudi Arabia Labour Market

Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market

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