Recommender Systems: A Multi-Disciplinary Approach by Monideepa Roy Paperback Bo

Description: Recommender Systems by Monideepa Roy, Pushpendu Kar, Sujoy Datta 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. FORMAT Paperback CONDITION Brand New Publisher Description 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 usesDescribes how to design, train, and evaluate a recommendation algorithmExplains migration from a recommendation model to a live system with usersDescribes utilization of the data collected from a recommender system to understand the user preferencesAddresses the security aspects and ways to deal with possible attacks to build a robust systemThis book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science. Author Biography Monideepa Roy, Pushpendu Kar, Sujoy Datta Table of Contents 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 Details ISBN1032333227 Author Sujoy Datta Pages 260 Publisher Taylor & Francis Ltd Series Intelligent Systems Year 2024 ISBN-13 9781032333229 Format Paperback Publication Date 2024-12-19 Imprint CRC Press Subtitle A Multi-Disciplinary Approach Place of Publication London Country of Publication United Kingdom Alternative 9781032333212 Edited by Sujoy Datta DEWEY 025.04 Illustrations 18 Tables, black and white; 48 Line drawings, black and white; 32 Halftones, black and white; 80 Illustrations, black and white Audience Tertiary & Higher Education UK Release Date 2024-12-19 ISBN-10 1032333227 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:168586294;

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