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 ebook constitutes the completely refereed post-workshop complaints of the ninth foreign Workshop on Mining net facts, 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 at the side of the thirteenth ACM SIGKDD foreign convention on wisdom Discovery and information Mining, KDD 2007.
The eight revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen from 23 preliminary submisssions. the improved papers tackle all present concerns in net mining and social community research, together with conventional internet and semantic net purposes, the rising purposes of the internet 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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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 on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers
We preprocess the texts in each post using the bag-of-words representation described before, and then calculate the averaged cosine similarity between 10 adjacent posts in a time-based consecutive order. In addition, we calculate the statistics of the number of unique contributors in a way that penalizes discussions involving very few contributors (to avoid random chatting between two persons). Figure 9 shows the discussion concentration with content similarity and the number of unique contributors.
We base it on the assumption that users associated with a larger set and frequency of cliques will then be ranked higher. Finally all of the calculated statistics are normalized and combined, each with an individual contribution to an overall social score with which the users are ultimately ranked. 1 G. Creamer et al. e. the number of emails sent and received, to be at least a limited indicator of importance. It is fair to hypothesize that users who communicate more, should, on average, maintain more important placement in the social hierarchy of the organization.
The eﬀect of adding relevance information in a relevance feedback environment. In: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Springer, Heidelberg (1994) 5. : Practical nonparametric statistics. John Wiley and Sons, New York (1971) 6. : Social roles in electronic communities. Internet Research 5, 19–22 (2004) 7. : Information diﬀusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web(WWW), pp.