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Recommender systems :algorithms and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Recommender systems :edited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty.
Reminder of title:	algorithms and applications /
other author:	Kumar, P. Pavan,
Description:	xvii, 230 pages :illustrations ;25 cm
Subject:	Recommender systems (Information filtering)
ISBN:	0367631857

Recommender systems :algorithms and applications /
Recommender systems :algorithms and applications /edited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty. - First edition. - xvii, 230 pages :illustrations ;25 cm

Includes bibliographical references and index.

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.

ISBN: 0367631857

CODEN: cam 2200433 i 4500

Nat. Bib. No.: GBC159348bnb

Nat. Bib. Agency Control No.: 020162501UkSubjects--Topical Terms:

310886
Recommender systems (Information filtering)

LC Class. No.: ZA3084 / .R43 2021

Dewey Class. No.: 005.5/6

Recommender systems :algorithms and applications /
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020 $a 0367631873 $q (paperback)
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245 0 0 $a Recommender systems : $b algorithms and applications / $c edited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty.
250 $a First edition.
264 1 $a Boca Raton, FL : $b CRC Press, $c 2021.
300 $a xvii, 230 pages : $b illustrations ; $c 25 cm
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504 $a Includes bibliographical references and index.
520 $a 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.
650 0 $a Recommender systems (Information filtering) $3 310886
650 6 $a Systèmes de recommandation (Filtrage d'information) $3 916374
700 1 $a Kumar, P. Pavan, $e editor. $3 916270
700 1 $a Vairachilai, S., $e editor. $3 916271
700 1 $a Potluri, Sirisha, $e editor. $3 916272
700 1 $a Mohanty, Sachi Nandan, $e editor. $3 916273
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