问答社区中基于问题粒度的用户专业性预测方法

In online community question answering (CQA), many raised questions under go long response time and lack high quality answers. There is a realistic need to measure the community users' professionalism degree on a specific problem. To date, previous methods based on link analysis or text analysis focused on only the professional metrics of community and topic, and did not fully investigate the question granularity. To address this issue, the concept of user professionalism based on question granularity in CQA was defined, and a prediction method for user professionalism based on question granularity was proposed, including a measurement method and a prediction model. Based on the community users' evaluation mechanism of answering qualities, the prediction method established professional metrics of users on the question granularity. Integrating together the user bias, the problem bias and the latent feedback of the question set that users answered, a model on problem granularity based on matrix factorization is constructed to predict how professional the user is in answering questions. By using the question-answer (QA) dataset under topics of Internet in Zhihu, comparative experiments with two mainstream methods were conducted. The results showed that the proposed measurement method of evaluating the degree of user professionalism is effective, and the prediction model has higher prediction accuracy.