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DAMDID 2023. S12T3. Nagim Davletshin. Information System for Predicting Personal Success
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[short] SESSION 12. Social Networks Analysis
Nagim Davletshin and Konstantin Nikolaev
Information System for Predicting Personal Success Based on Open Data from Social Networks
This paper presents the description and structure of an information and analytical system (IAS) aimed at collecting open quantitative and qualitative data of users of the social network on the Internet, as well as processing these data for use as input data for a complex neural network model for predicting professional and academic success. Methods for visualizing the results of the neural network model and methods for loading and unloading data from the IAS are also presented. Combining psychological and IT principles in the development of a neural network model and IAS offers a high-quality and reliable software product for predicting the success of users of the VK social network. The developed system can be used by HR companies to find the most suitable candidates, as well as by university admissions committees to find the most potentially successful applicants.
Nagim Davletshin and Konstantin Nikolaev
Information System for Predicting Personal Success Based on Open Data from Social Networks
This paper presents the description and structure of an information and analytical system (IAS) aimed at collecting open quantitative and qualitative data of users of the social network on the Internet, as well as processing these data for use as input data for a complex neural network model for predicting professional and academic success. Methods for visualizing the results of the neural network model and methods for loading and unloading data from the IAS are also presented. Combining psychological and IT principles in the development of a neural network model and IAS offers a high-quality and reliable software product for predicting the success of users of the VK social network. The developed system can be used by HR companies to find the most suitable candidates, as well as by university admissions committees to find the most potentially successful applicants.