Recommender Systems -

Recommender Systems

A Multi-Disciplinary Approach
Buch | Hardcover
260 Seiten
2023
CRC Press (Verlag)
978-1-032-33321-2 (ISBN)
149,60 inkl. MwSt
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.
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.

Monideepa Roy, Pushpendu Kar, Sujoy Datta

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

Erscheinungsdatum
Reihe/Serie Intelligent Systems
Zusatzinfo 18 Tables, black and white; 48 Line drawings, black and white; 32 Halftones, black and white; 80 Illustrations, black and white
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Gewicht 671 g
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Theorie / Studium Algorithmen
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Wirtschaft Volkswirtschaftslehre
ISBN-10 1-032-33321-9 / 1032333219
ISBN-13 978-1-032-33321-2 / 9781032333212
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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