| Document Type
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BL
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| Record Number
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1035164
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| Doc. No
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b789534
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| Main Entry
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Gündüz-Ögüdücü, Șule.
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| Title & Author
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Web page recommendation models : : theory and algorithms /\ Șule Gündüz-Ögüdücü.
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| Publication Statement
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San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :: Morgan & Claypool,, ©2011.
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| Series Statement
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Synthesis lectures on data management,; # 10
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| Page. NO
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1 online resource (viii, 77 pages :: illustrations
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| ISBN
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1608452484
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: 9781608452484
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9781608452477
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| Notes
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Part of: Synthesis digital library of engineering and computer science.
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Series from website.
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| Bibliographies/Indexes
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Includes bibliographical references (pages 69-75).
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| Contents
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1. Introduction to web page recommender systems.
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2. Preprocessing for web page recommender models -- Data collection -- Data preprocessing -- Web usage data preprocessing -- User and session identification -- Page time calculation -- Web content and structure data preprocessing -- Data model.
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3. Pattern extraction -- Collaborative filtering -- Learning user preferences -- Association rules -- Clustering -- Page clusters -- Session clusters -- Sequential patterns -- Combination of web page recommender systems -- Combination methods -- Semantic web.
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4. Evaluation metrics -- Bibliography -- Author's biography.
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| Abstract
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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 guide the 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.
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| Subject
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Recommender systems (Information filtering)
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| Subject
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Web sites-- Ratings and rankings.
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| Subject
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COMPUTERS-- Enterprise Applications-- Business Intelligence Tools.
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COMPUTERS-- Intelligence (AI) Semantics.
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| Subject
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Recommender systems (Information filtering)
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| Subject
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Web sites-- Ratings and rankings.
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| Dewey Classification
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006.3
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| LC Classification
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ZA4235.G858 2011
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