Download Advances in Web Mining and Web Usage Analysis: 8th by Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, PDF

By Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand

This booklet constitutes the completely refereed post-proceedings of the eighth foreign Workshop on Mining net information, WEBKDD 2006, held in Philadelphia, PA, united states in August 2006 at the side of the twelfth ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2006.

The thirteen revised complete papers awarded including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the e-book. the improved papers convey new applied sciences from components like adaptive mining tools, circulation mining algorithms, strategies for the Grid, specifically flat texts, files, images and streams, usability, e-commerce purposes, personalization, and advice engines.

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Extra info for Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20,

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HITS emphasizes on having a number of incoming links 24 K. Beemanapalli, R. Rangarajan, and J. Srivastava Fig. 3. Sample Link Structure of a Web Graph from related pages (good Authorities) rather than just having a large number of inlinks from unrelated pages. Figure 3, reproduced from [10] explains the concept of Hubs and Authorities. In [9] an extension to the Kleinberg’s algorithm using Matrix Exponentiation and Web log records is proposed. The key idea of this approach is to replace the adjacency link matrix used by the original algorithm by an exponential matrix using Taylor’s Series.

Com 15. : Web-log mining for quantitative temporal-event prediction. IEEE Computational Intelligence Bulletin 1(1), 10–18 (2002) 16. : Web-log mining for predictive web caching. edu Abstract. A number of methods exists that measure the distance between two web pages. Average-Clicks is a new measure of distance between web pages which fits user’s intuition of distance better than the traditional measure of clicks between two pages. Average-Clicks however assumes that the probability of the user following any link on a web page is the same and gives equal weights to each of the out-going links.

Ru,i − ru )2 ∀u∈Ui (2) (ru,j − r u )2 ∀u∈Uj Neighborhood size: The number, k, of nearest neighbors used for the neighborhood formation is important, because it can affect substantially the system’s accuracy. In most related works [8,22], k has been examined in the range of values between 10 and 100. , sparsity). Therefore, CF algorithms should be evaluated against varying k, in order to tune it. Nearest-Biclusters Collaborative Filtering with Constant Values 41 Positive rating threshold: Recommendation for a test user is performed by generating the top-N list of items that appear most frequently in his formed neighborhood (this method is denoted as Most-Frequent item-recommendation).

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