Skip to main content
SpringerLink
Log in

Open Source Software Recommendations Using Github

  • Conference paper
  • First Online:
Digital Libraries for Open Knowledge (TPDL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11057))

Included in the following conference series:

Abstract

The focus of this work is on providing an open source software recommendations using the Github API. Specifically, we propose a hybrid method that considers the programming languages, topics and README documents that appear in the users’ repositories. To demonstrate our approach, we implement a proof of concept that provides recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The work was partially supported by the TEKES Finnish project Virpa D.

  2. 2.

    https://help.github.com/articles/fork-a-repo/.

  3. 3.

    https://help.github.com/articles/about-repository-languages/.

  4. 4.

    Languages are automatically detected.

References

  1. Adomavicius, G., Kwon, Y.O.: Multi-criteria recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 847–880. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_25

    Chapter  Google Scholar 

  2. Kyriakidi, M., Stefanidis, K., Ioannidis, Y.E.: On achieving diversity in recommender systems. In: ExploreDB (2017)

    Google Scholar 

  3. Ntoutsi, E., Stefanidis, K., Rausch, K., Kriegel, H.: Strength lies in differences: diversifying friends for recommendations through subspace clustering. In: CIKM (2014)

    Google Scholar 

  4. Sandvig, J.J., Mobasher, B., Burke, R.D.: A survey of collaborative recommendation and the robustness of model-based algorithms. IEEE Data Eng. Bull. 31(2), 3–13 (2008)

    Google Scholar 

  5. Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems. ACM Trans. Database Syst. 36(3), 19:1–19:45 (2011)

    Article  Google Scholar 

  6. Stefanidis, K., Ntoutsi, E.: Cluster-based contextual recommendations. In: EDBT (2016)

    Google Scholar 

  7. Stefanidis, K., Pitoura, E., Vassiliadis, P.: Managing contextual preferences. Inf. Syst. 36(8), 1158–1180 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Tampere, Tampere, Finland

    Miika Koskela, Inka Simola & Kostas Stefanidis

Authors
  1. Miika Koskela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Inka Simola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kostas Stefanidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kostas Stefanidis .

Editor information

Editors and Affiliations

  1. University Carlos III, Madrid, Spain

    Eva Méndez

  2. USI, Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  3. INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal

    Cristina Ribeiro

  4. INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal

    Gabriel David

  5. INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal

    João Correia Lopes

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Koskela, M., Simola, I., Stefanidis, K. (2018). Open Source Software Recommendations Using Github. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J. (eds) Digital Libraries for Open Knowledge. TPDL 2018. Lecture Notes in Computer Science(), vol 11057. Springer, Cham. https://doi.org/10.1007/978-3-030-00066-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00066-0_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00065-3

  • Online ISBN: 978-3-030-00066-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation