Journal
PRICAI 2019: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I
Volume 11670, Issue -, Pages 364-376Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-29908-8_29
Keywords
Question answering forum; Churn; Flow theory; Computational user engagement; Reinforcement learning
Funding
- Research Grants Council of Hong Kong [16214817]
- Academy of Finland
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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.
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