Recommender Systems: A Multi-Disciplinary Approach: Intelligent Systems
Editat de Monideepa Roy, Pushpendu Kar, Sujoy Dattaen Limba Engleză Hardback – 19 iun 2023
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
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 311.58 lei 3-5 săpt. | +18.17 lei 10-14 zile |
Taylor & Francis Ltd. – 19 dec 2024 | 311.58 lei 3-5 săpt. | +18.17 lei 10-14 zile |
Hardback (2) | 920.33 lei 6-8 săpt. | |
Springer Nature Singapore – 12 iun 2024 | 1135.74 lei 3-5 săpt. | |
CRC Press – 19 iun 2023 | 920.33 lei 6-8 săpt. |
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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
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 PostgraduateCuprins
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