Towards Personalized Activity Level Prediction in Community Question Answering Websites

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 towards the communities of domain experts. While most research efforts have been made to identify experts or to preliminary detect potential experts of CQA websites, there has been a remarkable shift towards 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 paper, we present an algorithm termed PALP to predict the activity level of users of CQA websites. To the best of our knowledge, PALP is the first 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. CCS Concepts: rInformation systems→ Decision support systems; Data analytics;