Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique
This addresses tag classification and search efficiency for users in online communities, but is incremental as it adapts an existing technique to a new task.
The study tackled tag recommendation for online Q&A and open-source communities by applying the BERT pre-training technique for the first time, achieving higher accuracy and improved stability compared to baseline methods.
Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content. The most obvious advantage of tag recommendation is the correct classification of information. In this study, we used the BERT pre-training technique in tag recommendation task for online Q&A and open-source communities for the first time. Our evaluation on freecode datasets show that the proposed method, called TagBERT, is more accurate compared to deep learning and other baseline methods. Moreover, our model achieved a high stability by solving the problem of previous researches, where increasing the number of tag recommendations significantly reduced model performance.