High-quality domain expert finding method in CQA based on multi-granularity semantic analysis and interest drift

Abstract

Expert finding is an important research field in community question answering (CQA). Traditional expert finding methods mainly exploit topic analysis and authority calculation methods to identify high-quality experts in certain fields. To avoid recommending questions to those experts who do not display the willingness or ability to provide high-quality answers, user interest drift and user quality should be considered. This study proposes a novel method named high-quality domain expert finding in CQA based on multi-granularity semantic analysis and interest drift (HQExpert). Firstly, HQExpert considers different semantic granularities by employing two models, a coarse-grained topic model LC-LDA and a fine-grained model (BERT), to capture the domain information of questions and users more accurately. Secondly, to address the diverse interests of the users, a user interest drift model in HQExpert is developed to dynamically represent the changes in the interests of the users at different periods. In addition, a user quality model is developed to further optimize the professional level of the user, finding experts who can provide high-quality answers and are interested in the current question. Finally, extensive experiments on two datasets from different domains demonstrate that the proposed HQExpert model can significantly improve the accuracy of finding high-quality experts.