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MF-TagRec: Multi-feature Fused Tag Recommendation for GitHub

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

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Abstract

GitHub is one of the most popular hosting platforms for open-source projects, where tags are widely used to facilitate software organization and retrieval. However, the existences of inadequate and low-quality tags on GitHub hinder users from searching and retrieving their desired projects. In this paper, we propose MF-TagRec, an automatic tag recommendation method for projects by extracting multiple features from Readme documents, programming languages and dependency package tags of projects. We capture topics and global semantics of Readme documents as text features, along with programming languages and dependency package tags as word vector features. We construct a convolutional neural network and feed text and word vector features to predict the most relevant tags for untagged or few tag-assigned projects. We evaluate our proposed MF-TagRec on a real dataset GitHubDepDataSet compared with five baselines. The results show that MF-TagRec achieves Recall@5 and Recall@10 to 0.756 and 0.864 respectively, which outperforms the previous baselines.

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Acknowledgements

This work is being supported by the National Natural Science Foundation of China under the Grant No. 62172451, and supported by Scientific and Technological Innovation 2030-Major Project of New Generation Artificial Intelligence under the Grant No. 2020AAA0109601, by Open Research Projects of Zhejiang Lab under the Grant No.2022KG0AB01, and in part by the Natural Science Foundation of Hunan under the Grant No. 2020JJ4754 and 2020JJ5775.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Liu Yang, Ruo Yang, Tingxuan Chen & Hongxiao Fei

  2. Viterbi School of Engineering, University of Southern California, Los Angeles, USA

    Jiuqi Tang

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  1. Liu Yang
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  2. Ruo Yang
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  3. Tingxuan Chen
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  4. Hongxiao Fei
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  5. Jiuqi Tang
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Corresponding author

Correspondence to Tingxuan Chen .

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Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Yang, L., Yang, R., Chen, T., Fei, H., Tang, J. (2022). MF-TagRec: Multi-feature Fused Tag Recommendation for GitHub. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-20868-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20867-6

  • Online ISBN: 978-3-031-20868-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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