Community-based Question and Answering (CQA) platforms are nowadays enlightening over a billion people with crowdsourced knowledge. A key design issue in CQA platforms is how to find the potential answerers and to provide the askers timely and suitable answers, i.e., the so-called problem. State-of-art approaches often rely on extracting topics from the question texts. In this work, we analyze the question routing problem in a CQA system named Farm-Doctor that is exclusive for agricultural knowledge. The major challenge is that its questions contain limited textual information. To this end, we conduct an extensive measurement and obtain the whole knowledge repository of Farm-Doctor that consists of over 690 thousand questions and over 3 million answers. To remedy the text deficiency, we model Farm-Doctor as a heterogeneous information network that incorporates rich side information and based on network representation learning models we accurately recommend for each question the users that are highly likely to answer it. With an average income of fewer than 6 dollars a day, over 300 thousands farmers in China seek online in Farm-Doctor for agricultural advices. Our method helps these less eloquent farmers with their cultivation and hopefully provides a way to improve their lives.
A number of studies have addressed the question routing problem as a classification task. Yang @cite_14 proposed a probabilistic Topic Expertise Model which used tagging information and voting information to learn topical expertise estimation. They also proposed CQArank model that combines user topical expertise estimation and user authority derived from link analysis to find experts with both similar topical preference and high topical expertise.
@cite_14
2022149269
Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services to know how to find the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle this cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis. Based on TEM results, we proposed CQARank to measure user interests and expertise score under different topics. Leveraging the question answering history based on long-term community reviews and voting, our method could find experts with both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow data, the largest CQA focused on computer programming, show that our method achieves significant improvement over existing methods on multiple metrics.