  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
