eBook - ePub
Recommender Systems
A Multi-Disciplinary Approach
- 260 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Recommender Systems
A Multi-Disciplinary Approach
About this book
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.
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Yes, you can access Recommender Systems by Monideepa Roy, Pushpendu Kar, Sujoy Datta, Monideepa Roy,Pushpendu Kar,Sujoy Datta in PDF and/or ePUB format, as well as other popular books in Computer Science & Databases. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Series
- Title
- Copyright
- Contents
- About the Editors
- List of Contributors
- Foreword
- Preface
- Chapter 1 Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach
- Chapter 2 An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies
- Chapter 3 Neural Network-Based Collaborative Filtering for Recommender Systems
- Chapter 4 Recommendation System and Big Data: Its Types and Applications
- Chapter 5 The Role of Machine Learning/AI in Recommender Systems
- Chapter 6 A Recommender System Based on TensorFlow Framework
- Chapter 7 A Marketing Approach to Recommender Systems
- Chapter 8 Applied Statistical Analysis in Recommendation Systems
- Chapter 9 An IoT-Enabled Innovative Smart Parking Recommender Approach
- Chapter 10 Classification of Road Segments in Intelligent Traffic Management System
- Chapter 11 Facial Gestures-Based Recommender System for Evaluating Online Classes
- Chapter 12 Application of Swarm Intelligence in Recommender Systems
- Chapter 13 Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems
- Chapter 14 Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach
- Index