A methodology for workload characterization of E-commerce sites
Proceedings of the 1st ACM conference on Electronic commerce
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Understanding and mastering users role plays an important part in online public opinions tracking and electronic commerce marketing, etc. Different groups of users have different sociology attributes. Thus, it is very important and interesting to discover user role based on their sociology attributes. The present user role discovery methods are generally based on the structural features or static coarse-grained behavior features. In this paper, by analyzing a large number of real social network data, we propose a novel method for social role discovery based on sociology attributes features: we first mining and define several properties on behalf of sociology attributes; then, to deal with the sociology attributes features clustering, we use Bayesian information criterion as our stopping criterion; at last, the experimental results show that using this method can better understand user role in Sina Microblog. Besides, the methodology in this paper for user role discovery also can be applied to other social network in general.