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

  • Suman Kalyan MaityEmail author
  • Abhishek Panigrahi
  • Sayan Ghosh
  • Arundhati Banerjee
  • Pawan Goyal
  • Animesh Mukherjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)


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 TagCombine 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.


Tag recommendation Deep learning Stack Overflow 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Suman Kalyan Maity
    • 1
    Email author
  • Abhishek Panigrahi
    • 2
  • Sayan Ghosh
    • 2
  • Arundhati Banerjee
    • 2
  • Pawan Goyal
    • 2
  • Animesh Mukherjee
    • 2
  1. 1.Northwestern UniversityEvanstonUSA
  2. 2.Department of CSEIIT KharagpurKharagpurIndia

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