Recommender Systems: Algorithms and Applications

Recommender systems use information filtering to predict user preferences, becoming a vital part of e-business in various industries. This book explores theoretical underpinnings, algorithms, and applications, including machine learning, community detection, and filtering techniques. It also examines real-world systems for social networking, product recommendations, and risk prediction.

Format: Hardback
Length: 230 pages
Publication date: 04 June 2021
Publisher: Taylor & Francis Ltd

Recommender systems are a powerful tool that utilizes information filtering to accurately predict user preferences, making them an essential component of the e-business landscape. These systems find widespread application across diverse industries, including entertainment, social networking, information technology, tourism, education, agriculture, healthcare, manufacturing, and retail.

In "Recommender Systems: Algorithms and Applications," the book delves into the theoretical foundations of these systems and explores their practical implementation. It examines various classes of recommendation algorithms, such as machine learning algorithms, community detection algorithms, and filtering algorithms. By leveraging machine learning techniques, recommender systems can effectively filter and analyze unseen data, enabling users to make better predictions and informed decisions.

One of the key advantages of recommender systems is their ability to address challenges such as imbalanced data sets, cold-start problems, and long tail problems. These systems can handle large volumes of data and provide personalized recommendations to users, enhancing their overall experience and satisfaction.

Furthermore, the book explores fundamental ontological positions that form the basis of recommender systems. It explains why certain recommendations are predicted over others, shedding light on the underlying mechanisms and algorithms that drive these systems. Techniques and approaches for developing recommender systems are also investigated, including latent-factor techniques, collaborative filtering approaches, and content-based approaches.

The book provides a comprehensive examination of actual systems that utilize recommender systems, such as social networking, consumer product recommendation, and risk prediction in software engineering projects. By studying these real-world applications, readers gain valuable insights into the practical applications and benefits of recommender systems.

In conclusion, "Recommender Systems: Algorithms and Applications" is a valuable resource for anyone interested in understanding and developing recommender systems. It provides a comprehensive theoretical foundation, practical implementation techniques, and real-world applications, making it an essential tool for professionals and researchers in the field.

Weight: 522g
Dimension: 160 x 242 x 22 (mm)
ISBN-13: 9780367631857