Creating a Supplier Utility Metric and Researching it to Work with Segments
( Pp. 153-166)
More about authors
Natalia V. Grineva
Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Information Technology
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Maria A. Sukhan факультет ИТиАБД
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Natalia V. Kontsevaya Cand. Sci. (Econ.), Associate Professor
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Maria A. Sukhan факультет ИТиАБД
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Natalia V. Kontsevaya Cand. Sci. (Econ.), Associate Professor
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Abstract:
According to Marketplace Research 2022, the e-commerce segment is growing rapidly, but sellers don’t always understand what factors affect liquidity. Developing a recommendation system and set of measures based on seller usefulness will potentially improve service quality, positively impact the consumer experience, help the marketplace increase seller support and interaction with sellers, and improve the marketplace's competitiveness and reputation. The purpose of the study is to create a seller usefulness metric to further build a recommendation system and develop a set of measures to work with segments of sellers. Results: the algorithm for assessing the usefulness of the seller can be used to create a recommendation system and develop a set of measures that improve the quality of services and the potential profit of the site.
How to Cite:
Grineva N. V., Sukhan M. A., Kontsevaya N. V. Creating a Supplier Utility Metric and Researching it to Work with Segments // ECONOMIC PROBLEMS AND LEGAL PRACTICE. 2023. Vol. 19. № 3. P. 153-166. (in Russ.) EDN: FJTPPO
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Keywords:
SHAP, recommendation system, thresholding, logistic regression, SHAP, segmentation, utility, targeting, binary splitting..
