Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform
This work addresses quality evaluation for users of voice knowledge sharing platforms, but it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of evaluating voice knowledge sharing quality by proposing a data-driven approach for the Zhihu Live platform, demonstrating its effectiveness through experiments and releasing a dataset to support future research.
In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.