Toward Personalized Activity Level Prediction in Community Question Answering Websites

Abstract

Community Question Answering (CQA) websites have become valuable knowledge repositories. Millions of internet users resort to CQA websites to seek answers to their encountered questions. CQA websites provide information far beyond a search on a site such as Google due to (1) the plethora of high-quality answers, and (2) the capabilities to post new questions toward the communities of domain experts. While most research efforts have been made to identify experts or to preliminarily detect potential experts of CQA websites, there has been a remarkable shift toward investigating how to keep the engagement of experts. Experts are usually the major contributors of high-quality answers and questions of CQA websites. Consequently, keeping the expert communities active is vital to improving the lifespan of these websites. In this article, we present an algorithm termed PALP to predict the activity level of expert users of CQA websites. To the best of our knowledge, PALP is the first approach to address a personalized activity level prediction model for CQA websites. Furthermore, it takes into consideration user behavior change over time and focuses specifically on expert users. Extensive experiments on the Stack Overflow website demonstrate the competitiveness of PALP over existing methods.