Recommender systems algorithms and applications / edited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty.
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton : CRC Press, 2021. |
| Description | 1 online resource : illustrations (some color) |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Subjects |
| Other author/creator | Kumar, P. Pavan. |
| Other author/creator | Vairachilai, S. |
| Other author/creator | Potluri, Sirisha. |
| Other author/creator | Mohanty, Sachi Nandan. |
| Abstract | 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. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Biographical note | Dr. P. Pavan Kumar received a Ph. D. degree from JNTU, Anantapur, India. He is an Assistant Professor in the Department of Computer Science and Engineering at ICFAI Foundation for Higher Education (IFHE), Hyderabad. His research interests include real-time systems, multi-core systems, high-performance systems, computer vision. Dr. S. Vairachilai earned a Ph. D. degree in Information Technology from Anna University, India. She is an Assistant Professor in the Department of CSE at ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana. Prior to this she served in teaching roles an Kalasalingam University and N.P.R College of Engineering and Technology, Tamilnadu, India. Her research interests include Machine Learning, Recommender System and Social Network Analysis. Sirisha Potluri is an Assistant Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad. She is pursuing a Ph. D. degree in the area of cloud computing. Her research areas include distributed computing, cloud computing, fog computing, recommender systems and IoT. Dr. Sachi Nandan Mohanty received a Ph. D. degree from IIT Kharagpur, India. He is an Associate Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. Prof. Mohanty's research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. |
| Source of description | Print version record. |
| Issued in other form | Print version: RECOMMENDER SYSTEMS. [Place of publication not identified] : CRC PRESS, 2021 0367631857 |
| Genre/form | Electronic books. |
| LCCN | 2021762239 |
| ISBN | 9781000387278 (electronic bk.) |
| ISBN | 1000387275 (electronic bk.) |
| ISBN | 9780367631888 (electronic bk.) |
| ISBN | 0367631881 (electronic bk.) |
| ISBN | 9781000387377 (electronic bk. ; EPUB) |
| ISBN | 1000387372 (electronic bk. ; EPUB) |
| Stock number | 9780367631888 Taylor & Francis |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |