Abstract
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The dataset and source code are released on https://github.com/JiaYuan6/CSSR.
References
Chen, L., Zheng, A., Feng, Y., Xie, F., Zheng, Z.: Software service recommendation base on collaborative filtering neural network model. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 388–403. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_28
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST@EMNLP, pp. 103–111. Association for Computational Linguistics (2014)
GitHub: The state of the octoverse (2020). https://octoverse.github.com/
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR, pp. 1–10 (2016)
Jiang, J., Cheng, P., Wang, W.: Open source repository recommendation in social coding. In: Proceedings of SIGIR, pp. 1173–1176. ACM (2017)
Juan, Y., Zhuang, Y., Chin, W., Lin, C.: Field-aware factorization machines for CTR prediction. In: Proceedings of RecSys, pp. 43–50. ACM (2016)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of UAI, pp. 452–461. AUAI Press (2009)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW, pp. 285–295. ACM (2001)
Shao, H., et al.: paper2repo: GitHub repository recommendation for academic papers. In: Proceedings of WWW, pp. 629–639. ACM/IW3C2 (2020)
Sun, X., Xu, W., Xia, X., Chen, X., Li, B.: Personalized project recommendation on GitHub. Sci. China Inf. Sci. 61(5), 1–14 (2018)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of KDD, pp. 1225–1234. ACM (2016)
Acknowledgement
This work is partially supported by Australian Research Council Linkage Project (No.LP180100750) and Discovery Project (No.DP210100743).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, M., Liu, J., Zhang, W., Deng, K., Dong, H., Liu, Y. (2021). CSSR: A Context-Aware Sequential Software Service Recommendation Model. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-91431-8_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91430-1
Online ISBN: 978-3-030-91431-8
eBook Packages: Computer ScienceComputer Science (R0)