Recruitment System with Placement Prediction

Gupta, Suraj G.; Hingwala, Atif; Haryan, Yuvraj; Gharat, Swapnil

doi:10.1109/icais50930.2021.9395768

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…A number of decision trees are constructed by Random Forest, and then those trees are combined in order to provide a forecast that is both more accurate and more reliable. The process of recruiting will be made simpler and more effective by using this technique [8].…”

Section: Literature Studiesmentioning

confidence: 99%

A Review of System for Recommending Jobs Using Machine Learning

Y¹,

T²

2023

IJSREM

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The purpose of this research is to conduct a literature assessment of job recommender systems (JRS) that have been published in the recent past. When compared to our prior evaluations of the relevant literature, we placed a greater amount of importance on contributions that took into account the temporal and reciprocal aspects of job recommendations. Previous research on JRS has shown that it may be possible to enhance model performance by taking different perspectives like these into consideration when designing JRS. Additionally, it may result in a more balanced distribution of applicants among a group of occupations that are comparable to one another. In addition to this, we look at the literature from the point of view of the fairness of algorithms. When we looked into this, we discovered that this topic is seldom brought up in the academic literature, and when it does, many writers make the incorrect assumption that deleting the discriminating characteristic would be enough. When referring to the kinds of models that are used in JRS, writers usually refer to their approach as being "hybrid." In doing so, however, they unfortunately conceal what exactly these procedures include. We divided this expansive class of hybrids into more manageable subclasses by making use of the recommender taxonomies that were already in existence. In addition, we come to the conclusion that the availability of data, and more specifically the availability of click data, has a significant bearing on the selection of a validation technique. Last but not least, despite the fact that the generalizability of JRS across various datasets is seldom taken into consideration, the findings imply that error scores may change across these datasets. Keyword: Job Recommender Systems, Machine Learning , Businesses , Content Based Filtering , Gradient Boosting Regression Tree.

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Section: Literature Studiesmentioning

confidence: 99%

A Review of System for Recommending Jobs Using Machine Learning

Y¹,

T²

2023

IJSREM

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“…In [7], Naïve Bayes is used in the next step to determine or predict employee placement based on their characteristics. In [8], the authors proposed system aims to analyze the performance and possible suitable candidates for the job using the random forest method. According to [9], the authors set up a job predictor based on the candidate's resume.…”

Section: Introductionmentioning

confidence: 99%

Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company

Jayanti

Wasesa

2022

CoreIT

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Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes

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