Recommender systems : (Record no. 70806)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03684cam a2200553Ii 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9780367631888 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220711212248.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210520s2021 flua ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781000387278 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1000387275 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780367631888 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 0367631881 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781000387377 |
| -- | (electronic bk. : EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1000387372 |
| -- | (electronic bk. : EPUB) |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 005.5/6 |
| 245 00 - TITLE STATEMENT | |
| Title | Recommender systems : |
| Sub Title | algorithms and applications / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource : |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kumar, P. Pavan, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Vairachilai, S., |
| 700 1# - AUTHOR 2 | |
| Author 2 | Potluri, Sirisha, |
| 700 1# - AUTHOR 2 | |
| Author 2 | Mohanty, Sachi Nandan, |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://www.taylorfrancis.com/books/9780367631888 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Boca Raton : |
| -- | CRC Press, |
| -- | 2021. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 588 ## - | |
| -- | OCLC-licensed vendor bibliographic record. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Recommender systems (Information filtering) |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | COMPUTERS / Artificial Intelligence |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | COMPUTERS / Programming / Algorithms |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | BUSINESS & ECONOMICS / Consumer Behavior |
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