New PDF release: Advances in Web Mining and Web Usage Analysis: 9th

By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen

ISBN-10: 3642005276

ISBN-13: 9783642005275

This e-book constitutes the completely refereed post-workshop court cases of the ninth foreign Workshop on Mining net info, WEBKDD 2007, and the first overseas Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 along with the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and information Mining, KDD 2007.

The eight revised complete papers offered including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen from 23 preliminary submisssions. the improved papers handle all present matters in internet mining and social community research, together with conventional net and semantic internet functions, the rising purposes of the internet as a social medium, in addition to social community modeling and analysis.

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Extra resources for Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop

Sample text

Weighted clique score: A score computed using the “importance” of the people in each clique. This preliminary “importance” is computed strictly from the number of emails and the average response time. Each account in a clique is given a weight proportional to its computed preliminary. The weighted clique score is then computed by adding each weighed user contribution within the clique. Here the ’importance’ of the accounts in the clique raises the score of the clique. Segmentation and Automated Social Hierarchy Detection 45 More specifically, the raw clique score R is computed with the following formula: R = 2n−1 where n is the number of users in the clique.

Section 3 describes the specifics of the IBM Innovation Jam and the collected data. Section 4 summarizes some key aspects of the dynamics of the Jam interactions. Finally, Sections 5 and 6 describe respectively the unsupervised and supervised learning approaches we have applied to this data. 2 Related Work The Innovation Jam is a unique implementation of threaded discussions, whereby participants create topics and explicitly reply to each other using a “reply to” button. As such, discussions are hierarchical, with a clear flow of messages from the initial parent post to subsequent ideas and thoughts.

While recognizing that significant human processing took place in the course of evaluating Jam data, our goal is to see if we can identify factors that would have been predictive of the Jam finalists, perhaps suggesting ways to help make processes for future Jams less manually intensive. 2 Overview of the Jam Characteristics As mentioned above, the Innovation Jam was conducted in two phases that were separated by a period of less than 2 montha. Table 1 summarizes some of the basic statistics of these two phases.

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Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen


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