By Miklós Kurucz, András A. Benczúr (auth.), Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen (eds.)
This booklet constitutes the completely refereed post-workshop court cases of the ninth overseas Workshop on Mining internet info, WEBKDD 2007, and the first foreign 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 awarded including an in depth preface went via rounds of reviewing and development and have been rigorously chosen from 23 preliminary submisssions. the improved papers deal with all present concerns in net mining and social community research, together with conventional net and semantic internet purposes, the rising purposes of the net as a social medium, in addition to social community modeling and analysis.
Read or Download Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers PDF
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Additional 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 on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers
To compute S , we must ﬁrst scale and normalize 46 G. Creamer et al. each of the previous statistics which we have gathered. The contribution, C, of each metric is individually mapped to a [0, 100] scale and weighted with the following formula: wx · Cx = wx · 100 · xi − inf x sup x − inf x where x is the metric in question, wx is the respective weight for that metric, the sup x and inf x are computed across all i users and xi is the value for the user. This normalization is applied to each of the following metrics: 1.
Unlike previous IBM Jams where preparation was not necessary, the Jam required familiarization with emerging technologies which were described in online materials made available to participants prior to the event. Individual contributions to the Jam came in the form of “postings,” or messages in reply to other contributors and to questions posed under a moderated topic area. As shown in Figure 1, groups of such postings are deﬁned as “threads”. For ﬁve weeks following Phase 1 of the Innovation Jam, a multi-discipline, international cross-IBM team analyzed more than 37,000 Phase 1 posts to identify the most promising suggestions, resulting in 31 identiﬁed topics or “big ideas” as listed in Table 4.
O1 Average pairwise distance between the contributors within a big idea 4 . O2 Standard deviation of the pairwise distances between the contributors 4 . O3 Total number of pairwise distances between all the contributors involved. O4 Maximum pairwise distance between the contributors. O5 Minimum pairwise distance between the contributors. 046 Table 3. Description of 18 diﬀerent features used in the analysis of Innovation Jam 36 W. Gryc et al. Looking for Great Ideas: Analyzing the Innovation Jam 37 Table 4.