Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering

Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high in-flow and out-flow of users in these communities, a key challenge is to design effective strategies for recommending experts for new questions. This requires robust approaches that facilitate modeling users’ expertise given their changing interests and sparse historical data, at the same time being computationally less expensive for periodic updates. In this paper, we propose a simple graph diffusion-based expert recommendation model for CQA, that can outperform state-of-the-art convolutional neural network and transformers-based deep learning representatives and collaborative models. Our proposed method learns users’ expertise in the context of both semantic and temporal information to capture their changing interests and activity levels with time. Experiments on six real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of 50% performance gain compared to the best baseline method.