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 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.
S. K. Maity—Most of the work was done when all the authors were at IIT Kharagpur, India. We also acknowledge Prithwish Mukherjee, Shubham Saxena, Robin Singh, Chandra Bhanu Jha for helping us in various stages of this project.
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Notes
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The codes and data are available at https://bit.ly/2HsVhWC.
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Avg. length of questions is 129 words. For question length <300, we pad them with zero vectors.
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Maity, S.K., Panigrahi, A., Ghosh, S., Banerjee, A., Goyal, P., Mukherjee, A. (2019). DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_16
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