| 000 | 03306nam a22005655i 4500 | ||
|---|---|---|---|
| 001 | 978-981-15-2513-1 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125031.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 200302s2020 si | s |||| 0|eng d | ||
| 020 |
_a9789811525131 _9978-981-15-2513-1 |
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| 024 | 7 |
_a10.1007/978-981-15-2513-1 _2doi |
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| 050 | 4 | _aQA76.625 | |
| 072 | 7 |
_aUMW _2bicssc |
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| 072 | 7 |
_aCOM060160 _2bisacsh |
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| 072 | 7 |
_aUMW _2thema |
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| 082 | 0 | 4 |
_a006.76 _223 |
| 100 | 1 |
_aVenugopal, K. R. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aWeb Recommendations Systems _h[electronic resource] / _cby K. R. Venugopal, K. C. Srikantaiah, Sejal Santosh Nimbhorkar. |
| 250 | _a1st ed. 2020. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2020. |
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| 300 |
_aXXI, 164 p. 43 illus., 4 illus. in color. _bonline resource. |
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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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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1 Introduction -- 2 Web Data Extraction and Integration System for Search Engine Result Pages -- 3 Mining and Analysis of Web Sequential Patterns -- 4 Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web -- 5 Construction of Topic Directories using Levenshtein Similarity Weight -- 6 Related Search Recommendation with User Feedback Session -- 7 Webpage Recommendations based Web Navigation Prediction. . | |
| 520 | _aThis book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems. | ||
| 650 | 0 | _aInternet programming. | |
| 650 | 0 | _aComputer vision. | |
| 650 | 0 |
_aArtificial intelligence _xData processing. |
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| 650 | 0 | _aData mining. | |
| 650 | 1 | 4 | _aWeb Development. |
| 650 | 2 | 4 | _aComputer Vision. |
| 650 | 2 | 4 | _aData Science. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 700 | 1 |
_aSrikantaiah, K. C. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aSantosh Nimbhorkar, Sejal. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811525124 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811525148 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811525155 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-15-2513-1 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c173481 _d173481 |
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