Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users
This work addresses tag prediction to improve information organization and retrieval for users of community Q&A platforms, representing an incremental advancement in the field.
The paper tackled the problem of automatic tag prediction for questions on community Q&A platforms by analyzing user tagging behavior across 17 StackExchange communities, resulting in a neural architecture that effectively predicts both popular and granular tags with demonstrated performance gains.
In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model