High Precision Recommendation System for E-commerce Websites
Tewari, Anand Shanker
- 出版商: Independent Author
- 出版日期: 2023-02-14
- 售價: $1,270
- 貴賓價: 9.5 折 $1,207
- 語言: 英文
- 頁數: 168
- 裝訂: Quality Paper - also called trade paper
- ISBN: 4410027204
- ISBN-13: 9784410027208
-
相關分類:
推薦系統
海外代購書籍(需單獨結帳)
相關主題
商品描述
A thorough manual for creating a recommendation system for e-commerce websites that can offer consumers high accuracy and individualised product recommendations is available in "High Precision Recommendation System for E-commerce Websites" by Anand Shanker Tewari. The book offers a thorough examination of the many methods and algorithms employed in recommendation systems, as well as their applicability to e-commerce platforms.
The author discusses the value of recommendation systems in e-commerce and how they may contribute to higher consumer happiness and sales. The complete recommendation system development process is covered in the book, from data gathering and processing to model choice and evaluation. Additionally, it covers the drawbacks and shortcomings of recommendation systems and offers workable remedies for them.
The book's emphasis on highly accurate recommendations, which are essential for e-commerce companies to keep clients and foster loyalty, is one of its standout elements. Implementing collaborative filtering, content-based filtering, and hybrid techniques is covered in detail, along with code samples. Advanced subjects like real-time suggestions and deep learning-based recommendation systems are also covered in the book.
For those wishing to create a successful recommendation system for their e-commerce website, "High Precision Recommendation System for E-commerce Websites" is a must-read. It is a useful manual that offers a thorough grasp of the fundamental ideas and methods employed by recommendation systems and how they might be used in e-commerce.
The author discusses the value of recommendation systems in e-commerce and how they may contribute to higher consumer happiness and sales. The complete recommendation system development process is covered in the book, from data gathering and processing to model choice and evaluation. Additionally, it covers the drawbacks and shortcomings of recommendation systems and offers workable remedies for them.
The book's emphasis on highly accurate recommendations, which are essential for e-commerce companies to keep clients and foster loyalty, is one of its standout elements. Implementing collaborative filtering, content-based filtering, and hybrid techniques is covered in detail, along with code samples. Advanced subjects like real-time suggestions and deep learning-based recommendation systems are also covered in the book.
For those wishing to create a successful recommendation system for their e-commerce website, "High Precision Recommendation System for E-commerce Websites" is a must-read. It is a useful manual that offers a thorough grasp of the fundamental ideas and methods employed by recommendation systems and how they might be used in e-commerce.