Description

Book Synopsis

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.



Table of Contents

1. Pricing based on product descriptions: problem, data, and methods.- 2. Extract product data from descriptions by NLP techniques.- 3. Segmentation and Quantity the qualify features.- 4. Pricing prediction using machine learning and ensemble methods.- 5. Applications & Discussions.

Artificial Intelligence for Automated Pricing

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    Order before 4pm today for delivery by Mon 29 Jun 2026.

    A Paperback / softback by Nguyen Thi Ngoc Anh, Tran Ngoc Thang, Vijender Kumar Solanki

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      Publisher: Springer Verlag, Singapore
      Publication Date: 29/08/2021
      ISBN13: 9789811647017, 978-9811647017
      ISBN10: 9811647011
      Also in:
      Artificial intelligence (AI) Machine learning Mathematical modelling

      Description

      Book Synopsis

      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.



      Table of Contents

      1. Pricing based on product descriptions: problem, data, and methods.- 2. Extract product data from descriptions by NLP techniques.- 3. Segmentation and Quantity the qualify features.- 4. Pricing prediction using machine learning and ensemble methods.- 5. Applications & Discussions.

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