Requirements: .ePUB, .PDF reader, 19.9 MB
Overview: The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense. Recommendation systems gather information about the likes and dislikes of a user and use various types of complex algorithms to predict what a user may be interested in and send personalized recommendations to users. Brands like Netflix, Amazon, Facebook, Spotify, and YouTube collect information about users and try to predict user preferences. If a person buys a certain product, then suggestions for similar products are sent to the user. If a user likes a particular type of music or movie, then it will try to predict and recommend similar types of music or movies to the user. It is a very vast and interesting area of research but at present, in this book, we have taken some of the most important topics which form the basis of recommender systems, along with some case studies and applications and suggestions for future research directions. This book will be useful to users who are new to the topic and wish to learn it. It will also be useful to advanced users who know the theory but want to implement or design a system from scratch and can learn from the different types of algorithms.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://ouo.io/FLYNFP
https://ouo.io/egc9Y4v.
