Tracking user-role evolution via topic modeling in community question answering

Abstract

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.