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Titlebook: Web Page Recommendation Models; Şule Gündüz-Ögüdücü Book 2011 Springer Nature Switzerland AG 2011

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发表于 2025-3-21 19:49:01 | 显示全部楼层 |阅读模式
书目名称Web Page Recommendation Models
编辑Şule Gündüz-Ögüdücü
视频video
丛书名称Synthesis Lectures on Data Management
图书封面Titlebook: Web Page Recommendation Models; Şule Gündüz-Ögüdücü Book 2011 Springer Nature Switzerland AG 2011
描述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 ov
出版日期Book 2011
版次1
doihttps://doi.org/10.1007/978-3-031-01842-8
isbn_softcover978-3-031-00714-9
isbn_ebook978-3-031-01842-8Series ISSN 2153-5418 Series E-ISSN 2153-5426
issn_series 2153-5418
copyrightSpringer Nature Switzerland AG 2011
The information of publication is updating

书目名称Web Page Recommendation Models影响因子(影响力)




书目名称Web Page Recommendation Models影响因子(影响力)学科排名




书目名称Web Page Recommendation Models网络公开度




书目名称Web Page Recommendation Models网络公开度学科排名




书目名称Web Page Recommendation Models被引频次




书目名称Web Page Recommendation Models被引频次学科排名




书目名称Web Page Recommendation Models年度引用




书目名称Web Page Recommendation Models年度引用学科排名




书目名称Web Page Recommendation Models读者反馈




书目名称Web Page Recommendation Models读者反馈学科排名




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Pattern Extraction,ering, (4) sequential patterns, (5) combination of Web page recommender systems, and (6) semantic Web. All the techniques are described in terms of their applicability for usage pattern extraction in a Web page recommender system rather than their general usage in a data mining application.
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Evaluation Metrics, training and test sets. Alternatively, the data set can be partitioned into training and test sets according to time periods. The model is built using the training set whereas the accuracy results are given on test set.
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Evaluation Metrics, training and test sets. Alternatively, the data set can be partitioned into training and test sets according to time periods. The model is built using the training set whereas the accuracy results are given on test set.
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Preprocessing for Web Page Recommender Models,uced. Web page recommender systems accept user models and a set of potential Web pages that can be recommended as input, and they generate a subset of these pages as output (Figure 2.1). Thus, recommender systems can be characterized by how they model users: explicitly or implicitly. In explicit use
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Pattern Extraction,gorithms based on the data mining techniques they use for user modeling. In most of the methods, techniques are combined together for discovering usage patterns. Some of the works are proposed only for user modeling, and the discovered patterns are not used for recommendation. However, the discovere
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