Haoyu Wang
ABSTRACT
Stack Overflow is one of the most popular online programming question and answer websites for developers around the world. Generally, developers need to provide tags for their posting. High-quality tags are expected to facilitate correct classification and efficient search. Unfortunately, tagging process is distributed and uncoordinated due to developers' understanding of their postings, English skills and preferences. Automatic tag recommendation becomes increasingly important for these information sites. In this paper, we propose SOTagRec, a novel tag recommendation approach combing convolutional neural network model and collaborative filtering method. By learning historical postings and their tags from existing information, SOTagRec can accurately infer tags for new postings. We have evaluated SOTagRec on Stackoverflow and compare with the state-of-the-art methods. Experiments Results show that SOTagRec achieves 81.7% and 88.7% respectively for Recall@5 and Recall@10, which outperforms the previous relevant methods.
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Index Terms
- SOTagRec: A Combined Tag Recommendation Approach for Stack Overflow
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