Multi-View Scholar Clustering With Dynamic Interest Tracking

Scholar clustering has garnered increasing attention due to the explosive growth of scholar data. Although researchers have proposed many algorithms to cluster scholars, they typically focus on clustering scholars from the intrinsic view (scholars’ contents). These algorithms may lead to inaccurate and biased clustering results because they ignore the extrinsic view (scholar's specialty) and the changeability of scholars’ interest in each view. In this paper, we propose a multi-view scholar clustering topic model (MSCT), which integrates complementary information from both intrinsic and extrinsic views while considering dynamic scholar interests. Specifically, MSCT involves two novel schemes. The first one is
multi-view integration
, where MSCT collaboratively tracks scholars’ time-varying topic distribution from two views:
intrinsic view
and
extrinsic view
. The former exploits the details of different academic degrees in the title and information in the abstract; the latter leverages the specialty of different categories in the corresponding research field and research discipline. The second one is
dynamic interest tracking
, which dynamically models each scholar's interest distribution in terms of the current scholar texts and previously estimated distribution through a newly designed collapsed Gibbs sampling algorithm. Experimental results demonstrate that MSCT can significantly outperform state-of-the-art algorithms.