000 03548nam a22004935i 4500
001 978-3-031-01842-8
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005 20240730163436.0
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008 220601s2011 sz | s |||| 0|eng d
020 _a9783031018428
_9978-3-031-01842-8
024 7 _a10.1007/978-3-031-01842-8
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aGunduz-Oguducu, Sule.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978552
245 1 0 _aWeb Page Recommendation Models
_h[electronic resource] /
_cby Sule Gunduz-Oguducu.
250 _a1st ed. 2011.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2011.
300 _aVII, 77 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aIntroduction to Web Page Recommender Systems -- Preprocessing for Web Page Recommender Models -- Pattern Extraction -- Evaluation Metrics.
520 _aOne of the application areas of data mining is the World Wide Web (WWW or Web), which serves as a huge, widely distributed, global information service for every kind of information such as news, advertisements, consumer information, financial management, education, government, e-commerce, health services, and many other information services. The Web also contains a rich and dynamic collection of hyperlink information, Web page access and usage information, providing sources for data mining. The amount of information on the Web is growing rapidly, as well as the number of Web sites and Web pages per Web site. Consequently, it has become more difficult to find relevant and useful information for Web users. Web usage mining is concerned with guiding the Web users to discover useful knowledge and supporting them for decision-making. In that context, predicting the needs of a Web user as she visits Web sites has gained importance. The requirement for predicting user needs in order to guidethe user in a Web site and improve the usability of the Web site can be addressed by recommending pages to the user that are related to the interest of the user at that time. This monograph gives an overview of the research in the area of discovering and modeling the users' interest in order to recommend related Web pages. The Web page recommender systems studied in this monograph are categorized according to the data mining algorithms they use for recommendation. Table of Contents: Introduction to Web Page Recommender Systems / Preprocessing for Web Page Recommender Models / Pattern Extraction / Evaluation Metrics.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_978553
650 2 4 _aData Structures and Information Theory.
_931923
710 2 _aSpringerLink (Online service)
_978554
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007149
776 0 8 _iPrinted edition:
_z9783031029707
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_978555
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01842-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84609
_d84609