Multi-View Scholar Clustering With Dynamic Interest Tracking
Ang Li, Yawen Li, Yingxia Shao, Bingyan Liu · IEEE Transactions on Knowledge and Data Engineering · 2023
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 ismulti-view integration, where MSCT collaboratively tracks scholars’ time-varying topic distribution from two views:intrinsic viewandextrinsic 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 isdynamic 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.