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• W4285163952 abstract "Verification of candidate profile is most challenging job for HR professional as it involves a lot of time and other resources. Verification of credentials needs to be done before the job offer is made, and if any discrepancy is found at a later stage, the entire process of recruitment loses its credibility. This can result in a loss of money, time and other resources for the organizations. Graph-based analysis using NoSQL software has proved to be extremely helpful in the verification of the credentials at the very beginning of the recruitment process, thereby saving a lot of resources and enhancing the credibility of the process. Graph databases help in identification of patterns in semi-structured and unstructured data. By analysing the data gathered from social media platforms, with the help of graph-based tools, deeper and newer insights about candidate’s profile can be generated. Nodes of the graph-based NoSQL can store various heterogeneous data for map, analyse it and create connection between datasets. HR analytics can further help recruiters in intelligent decision-making and in improving the efficiency and quality of talent acquisition process. Various factors like demographical information, educational qualification, employment history, behaviour, communication records, etc. are taken into consideration for deciding the candidature and suitability of the prospective employee. Objective of the paper is to provide a model to verify and validate candidate’s profile before recruitment process begins. Keeping this objective in mind, authors propose a graph-based model for analysing this heterogeneous data used during recruitment process with the help of nodes for better, quick and authentic verification of data given by the candidate. Authors used algorithms like clustering and association to establish relationship among nodes and develop various insights about the suitability and fit of the candidate. This model would be helpful for recruiters in differentiating and segregating candidates based on graphical prediction and would also come handy in sorting the huge list of applicants quickly and accurately. At last, authors also discuss the limitations of the model." @default.
• W4285163952 created "2022-07-14" @default.
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• W4285163952 date "2022-01-01" @default.
• W4285163952 modified "2023-09-23" @default.
• W4285163952 title "Analysis of Factor Verification Affecting Recruitment Process Through Social Dynamics" @default.
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• W4285163952 doi "https://doi.org/10.1007/978-981-19-0898-9_13" @default.
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