DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow
This work addresses tag recommendation for Stack Overflow users, presenting a novel method that improves accuracy but is incremental in nature.
The paper tackles the problem of recommending question tags on Stack Overflow by developing DeepTagRec, a deep learning framework that fuses content and user representations, resulting in significant performance gains such as 60.8% improvement in precision@3 and 36.8% in recall@10 over the best baseline.
In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow. The proposed system learns the content representation from question title and body. Subsequently, the learnt representation from heterogeneous relationship between user and tags is fused with the content representation for the final tag prediction. On a very large-scale dataset comprising half a million question posts, DeepTagRec beats all the baselines; in particular, it significantly outperforms the best performing baseline T agCombine achieving an overall gain of 60.8% and 36.8% in precision@3 and recall@10 respectively. DeepTagRec also achieves 63% and 33.14% maximum improvement in exact-k accuracy and top-k accuracy respectively over TagCombine