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roy m recommender systems multi disciplinary approach 2023

Type:: E-books
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Uploaded On:: April 25, 2023, 11:58 a.m.
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Textbook in PDF format 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. A recommender system, or a recommendation system, is a subclass of information fltering systems that predicts the “rating” or “preference” a user would give to an item. They are primarily used for commercial applications. They are most commonly recognized as playlist generators for video and music services like Netfix, YouTube, and Spotify; product recommenders for services such as Amazon; or content recommenders for social media platforms such as and Twitter. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. There are also popular recommender systems for specifc topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and fnancial services. 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 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 Classifcation of Road Segments in Intelligent Traffc 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

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roy m recommender systems multi disciplinary approach 2023

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