Cantitate/Preț
Produs

Recommender Systems: A Multi-Disciplinary Approach: Intelligent Systems

Editat de Monideepa Roy, Pushpendu Kar, Sujoy Datta
en Limba Engleză Hardback – 19 iun 2023
Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data.
Features of this book:
  • Identifies and describes recommender systems for practical uses
  • Describes how to design, train, and evaluate a recommendation algorithm
  • Explains migration from a recommendation model to a live system with users
  • Describes utilization of the data collected from a recommender system to understand the user preferences
  • Addresses the security aspects and ways to deal with possible attacks to build a robust system
This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 31158 lei  3-5 săpt. +1817 lei  10-14 zile
  Taylor & Francis Ltd. – 19 dec 2024 31158 lei  3-5 săpt. +1817 lei  10-14 zile
Hardback (2) 92033 lei  6-8 săpt.
  Springer Nature Singapore – 12 iun 2024 113574 lei  3-5 săpt.
  CRC Press – 19 iun 2023 92033 lei  6-8 săpt.

Preț: 92033 lei

Preț vechi: 115040 lei
-20% Nou

Puncte Express: 1380

Preț estimativ în valută:
17613 18296$ 14630£

Carte tipărită la comandă

Livrare economică 01-15 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9781032333212
ISBN-10: 1032333219
Pagini: 278
Ilustrații: 18 Tables, black and white; 48 Line drawings, black and white; 32 Halftones, black and white; 80 Illustrations, black and white
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.67 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Intelligent Systems


Public țintă

Academic and Postgraduate

Cuprins

1. Comparison of Different Machine Learning  Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach; 2. An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies; 3.  Neural Network-Based Collaborative Filtering for Recommender Systems; 4. Recommendation System and Big Data: Its Types and Applications; 5. The Role of Machine Learning /AI in Recommender Systems; 6. A Recommender System Based on TensorFlow Framework; 7. A Marketing Approach to Recommender Systems; 8. Applied Statistical Analysis in Recommendation Systems; 9.  An IoT-Enabled Innovative Smart Parking Recommender Approach; 10. Classification of Road Segments in Intelligent Traffic Management System; 11. Facial Gestures-Based Recommender System for Evaluating Online Classes; 12. Application of Swarm Intelligence in Recommender Systems; 13. Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems; 14. Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach

Notă biografică

Monideepa Roy, Pushpendu Kar, Sujoy Datta

Descriere

This book presents a multi-disciplinary approach for development of Recommender Systems. It explains different types of pertinent algorithms with their comparative analysis, and their role for different applications including case studies. It explains Big Data behind Recommender System, making good decision support systems, etc.

Textul de pe ultima copertă

The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense.

Caracteristici

Studies different types of algorithms for recommender systems along with their comparative analysis Presents case studies of the application of recommender system in healthcare monitoring and military surveillance Shows how to design attack-resistant and trust-centric recommender systems for applications dealing with sensitive data