HRCR: Hidden Markov-Based Reinforcement to Reduce Churn in Question Answering Forums

The high rate of churning users who abandon the Community Question Answering forums (CQAs) may be one of the crucial issues that hinder their development. More personalized question recommendation to users might help to manage this problem better. In this paper, we propose a new algorithm (we name HRCR) that recommends questions to users such to reduce their churning probability. We present our algorithm in a two-fold structure: First, we use Hidden Markov Models (HMMs) to uncover the users' engagement states inside a CQA. Second, we apply a Reinforcement Learning Model (RL) to recommend users the questions that match better with their engagement mood and thus help them get into a better engagement state (the one with the least churning probability). Experiments on a large-scale offline dataset from Stack Overflow show a meaningful reduction in the churning probability of the users who comply with HRCR's question recommendations.