# DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow

arXiv: https://arxiv.org/abs/1903.03941 · HTML: https://mlsanity.com/paper/1903.03941/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Semantically-enhanced Topic Recommendation System for Software Projects](https://mlsanity.com/paper/2206.00085/) (2022)
- [Studying and Recommending Information Highlighting in Stack Overflow Answers](https://mlsanity.com/paper/2401.01472/) (2024)
- [Characterizing and Classifying Developer Forum Posts with their Intentions](https://mlsanity.com/paper/2312.14279/) (2023)

## Abstract (arXiv)

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