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Web Page Recommendation Models [electronic resource] / by Sule Gunduz-Oguducu.

By: Gunduz-Oguducu, Sule [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Data Management: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2011Edition: 1st ed. 2011.Description: VII, 77 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031018428.Subject(s): Computer networks  | Data structures (Computer science) | Information theory | Computer Communication Networks | Data Structures and Information TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 004.6 Online resources: Click here to access online
Contents:
Introduction to Web Page Recommender Systems -- Preprocessing for Web Page Recommender Models -- Pattern Extraction -- Evaluation Metrics.
In: Springer Nature eBookSummary: One 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.
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Introduction to Web Page Recommender Systems -- Preprocessing for Web Page Recommender Models -- Pattern Extraction -- Evaluation Metrics.

One 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.

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