An author subject topic model for expert recommendation

Abstract

A supervised hierarchical topic model, named the Author Subject Topic (AST) model, was introduced for expert recommendation in this study. The difference between the Author Topic (AT) model and the AST model is that the AST model introduces an additional supervised “Subject” layer. The additional supervised layer of AST allows subjects to be shared across authors and group documents under various topic distributions, rather than only grouping documents under a single author’s topic distribution, which encourages to cluster documents and words with less noise. In considerations that interdisciplinary studies are a major trend in many research fields, a typical interdisciplinary, Information Management and Information System, is investigated and corresponding real data were gathered from WANFANG DATA (http://www. wanfangdata.com.cn/). Different comparative experiments were conducted, which demonstrates that the AST model outperforms the AT model on this dataset. It shows that the AST model is able to capture the subject class and distinguish the topics effectively for modeling the expert’s research interests, which helps for expert recommendation.