RANKING AUTHORS WITH LEARNING-TO-RANK TOPIC MODELING

摘要

Topic modeling has emerged as a popular learning technique not only in mining text representations, but also in modeling authors' interests and influence, as well as predicting linkage among documents or authors. However, few existing topic models distinguish and make use of the prior knowledge in regard to the different importance of documents (authors) over topics. In this paper, we focus on the ability of topic models in modeling author interests and influence. We introduce a pair-wise based learning-to-rank algorithm into the topic modeling process with the hypothesis that investigating and exploring the prior-knowledge on authors' different importance over topics can help to achieve more accurate and cohesive topic modeling results. Moreover, the framework integrating learning-to-rank mechanism with topic modeling can help to facilitate ranking in new authors. In this paper, we particularly apply this integrated model into two applications: the task of predicting future award winners of research communities, and predicting future PC members of scientific conferences. Experiments based on two real world data sets demonstrate that our proposed model can achieve competitive ranking performance with several state-of-the-art learning-to-rank or topic modeling algorithms.