Artificial Intelligence for Automated Pricing Based on Product Descriptions (SpringerBriefs in Applied Sciences and Technology) - Softcover

Über die Autorin bzw. den Autor

Nguyen Thi Ngoc Anh received her Ph.D. in Artificial Intelligence at Paris 6 University, France, in 2014; she was researcher of UMI 209, UMMISCO, Vietnam, from 2010 to 2018; she is also Lecturer in the University of Technology, Vietnam, since 2003. Her research concentrates on the prediction of time series, anomaly detection, stochastic processes, machine learning, and data mining. She has more than ten years of academic experience in an agent-based model, stochastics processes, data analytics, machine learning, and data mining.

Tran Ngoc Thang received his Ph.D. in Applied Mathematics from Hanoi University of Science and Technology in 2016. Since 2009, he has been Lecturer at the School of Applied Mathematics and Informatics, Hanoi University of Science and Technology.  His research interests are in the area of optimization, artificial intelligence, data analytics, knowledge-based systems, soft computing, evolutionary computation, combinatorial optimization, multi-objective optimization, and stochastic optimization.

Vijender Kumar Solanki is Associate Professor of Computer Science and Engineering at the CMR Institute of Technology (Autonomous), Hyderabad, TS, India. He has more than ten years of academic experience in network security, IoT, big data, smart city, and IT. Before his current role, he was associated with Apeejay Institute of Technology, Greater Noida, UP, KSRCE (Autonomous) Institution, Tamilnadu, India, and Institute of Technology and Science, Ghaziabad, UP, India. He is Member of ACM and Senior Member of IEEE. He has authored or co-authored more than 50 research articles that are published in various journals, books, and conference proceedings. He has edited or co-edited 14 books and conference proceedings in the area of soft computing.

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This book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.