Topic taxonomy adaptation for group profiling

Lei Tang, Huan Liu, Jianping Zhang, Nitin Agarwal, John J. Salerno
2008 ACM Transactions on Knowledge Discovery from Data  
A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a group's interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the
more » ... namic change of group's interest. This paper addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflect the change of group's interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approach's efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.
doi:10.1145/1324172.1324173 fatcat:lacaurrk75c2vl2wrr36az3qqy