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

## TL;DR

DeepTagRec is a deep learning framework that effectively recommends question tags on Stack Overflow by combining content analysis with user-tag relationship modeling, outperforming existing methods on large-scale data.

## Contribution

It introduces a novel content-cum-user based deep learning approach that significantly improves tag recommendation accuracy over baseline models.

## Key findings

- Outperforms baseline models with 60.8% precision@3 improvement
- Achieves 36.8% recall@10 enhancement
- Demonstrates significant gains in exact-k and top-k accuracy

## Abstract

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