Название: Recommender Systems: A Multi-Disciplinary ApproachАвтор: Monideepa Roy, Pushpendu Kar, Sujoy Datta
Издательство: CRC Press
Серия: Intelligent Systems
Год: 2023
Страниц: 279
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
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 Facebook 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
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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