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Recommender Systems - Algorithms and Applications (Hardcover)
Loot Price: R3,220
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Recommender Systems - Algorithms and Applications (Hardcover)
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Recommender systems use information filtering to predict user
preferences. They are becoming a vital part of e-business and are
used in a wide variety of industries, ranging from entertainment
and social networking to information technology, tourism,
education, agriculture, healthcare, manufacturing, and retail.
Recommender Systems: Algorithms and Applications dives into the
theoretical underpinnings of these systems and looks at how this
theory is applied and implemented in actual systems. The book
examines several classes of recommendation algorithms, including
Machine learning algorithms Community detection algorithms
Filtering algorithms Various efficient and robust product
recommender systems using machine learning algorithms are helpful
in filtering and exploring unseen data by users for better
prediction and extrapolation of decisions. These are providing a
wider range of solutions to such challenges as imbalanced data set
problems, cold-start problems, and long tail problems. This book
also looks at fundamental ontological positions that form the
foundations of recommender systems and explain why certain
recommendations are predicted over others. Techniques and
approaches for developing recommender systems are also
investigated. These can help with implementing algorithms as
systems and include A latent-factor technique for model-based
filtering systems Collaborative filtering approaches Content-based
approaches Finally, this book examines actual systems for social
networking, recommending consumer products, and predicting risk in
software engineering projects.
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