000 | 03684cam a2200553Ii 4500 | ||
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001 | 9780367631888 | ||
003 | FlBoTFG | ||
005 | 20220711212248.0 | ||
006 | m o d | ||
007 | cr cnu---unuuu | ||
008 | 210520s2021 flua ob 001 0 eng d | ||
040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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020 |
_a9781000387278 _q(electronic bk.) |
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020 |
_a1000387275 _q(electronic bk.) |
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020 |
_a9780367631888 _q(electronic bk.) |
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020 |
_a0367631881 _q(electronic bk.) |
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020 |
_a9781000387377 _q(electronic bk. : EPUB) |
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020 |
_a1000387372 _q(electronic bk. : EPUB) |
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020 | _z0367631857 | ||
020 | _z9780367631857 | ||
035 | _a(OCoLC)1251926845 | ||
035 | _a(OCoLC-P)1251926845 | ||
050 | 4 | _aZA3084 | |
072 | 7 |
_aCOM _x004000 _2bisacsh |
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072 | 7 |
_aCOM _x051300 _2bisacsh |
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072 | 7 |
_aBUS _x016000 _2bisacsh |
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072 | 7 |
_aUYQ _2bicssc |
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082 | 0 | 4 |
_a005.5/6 _223 |
245 | 0 | 0 |
_aRecommender systems : _balgorithms and applications / _cedited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty. |
250 | _aFirst edition. | ||
264 | 1 |
_aBoca Raton : _bCRC Press, _c2021. |
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300 |
_a1 online resource : _billustrations (some color) |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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520 | _aRecommender 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. | ||
588 | _aOCLC-licensed vendor bibliographic record. | ||
650 | 0 |
_aRecommender systems (Information filtering) _99125 |
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650 | 7 |
_aCOMPUTERS / Artificial Intelligence _2bisacsh _912740 |
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650 | 7 |
_aCOMPUTERS / Programming / Algorithms _2bisacsh _913132 |
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650 | 7 |
_aBUSINESS & ECONOMICS / Consumer Behavior _2bisacsh _914827 |
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700 | 1 |
_aKumar, P. Pavan, _eeditor. _914828 |
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700 | 1 |
_aVairachilai, S., _eeditor. _914829 |
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700 | 1 |
_aPotluri, Sirisha, _eeditor. _914830 |
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700 | 1 |
_aMohanty, Sachi Nandan, _eeditor. _914831 |
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856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780367631888 |
856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
942 | _cEBK | ||
999 |
_c70806 _d70806 |