Abstract
GitHub is a hosting platform for open-source software projects, where developers can share their open-source projects with others in the form of a repository. However, as the software projects hosted on the platform increase, it becomes difficult for developers to find software projects that meet their need or interest. Considering the practical importance of software project recommendations, we propose a recommendation method based on deep matrix factorization and apply it to GitHub, which is used to recommend personalized software projects in GitHub. With the use of deep neural network, we learn a low dimensional representation of users and projects from user-project matrix in a common space, in which we can capture the user’s latent behavior preference of each item, and automatically recommend the top N personalized software projects. The experiments on use-project data extracted from GitHub shows that the proposed recommendation method can recommend more accurate results compared with other three recommendation methods, i.e., UCF (user collaborative filtering), ICF (item collaborative filtering) and PPR (a personalized recommendation method).
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References
Github: the 2020 state of octoverse report. https://octoverse.github.com (2020)
Chen, L., Zheng, A., Feng, Y., Xie, F., Zheng, Z.: Software service recommendation base on collaborative filtering neural network model. In: 16th International Conference, ICSOC 2018, Hangzhou, China, November 12–15, 2018, Proceedings (2018)
Dellal-Hedjazi, B., Alimazighi, Z.: Deep learning for recommendation systems. In: 6th IEEE Congress on Information Science and Technology, CiSt 2020, Agadir - Essaouira, Morocco, 5–12 June 2021. pp. 90–97. IEEE (2021)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, pp. 278–288 (2015)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
Guendouz, M., Amine, A., Hamou, R.M.: Recommending relevant open source projects on github using a collaborative-filtering technique. Int. J. Open Source Softw. Process. 6(1), 1–16 (2015)
Han, J., Deng, S., Xia, X., Wang, D., Yin, J.: Characterization and prediction of popular projects on github. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (2019)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)
Koskela, M., Simola, I., Stefanidis, K.: open source software recommendations using github. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J.C. (eds.) TPDL 2018. LNCS, vol. 11057, pp. 279–285. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00066-0_24
Lian, J., Zhang, F., Xie, X., Sun, G.: Cccfnet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 817–818 (2017)
Liu, C., Yang, D., Zhang, X., Ray, B., Rahman, M.M.: Recommending github projects for developer onboarding. IEEE Access 6, 52082–52094 (2018)
Matek, T., Zebec, S.T.: Github open source project recommendation system. arXiv preprint arXiv:1602.02594 (2016)
Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240–248 (2020)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Sun, X., Xu, W., Xia, X., Chen, X., Li, B.: Personalized project recommendation on github. Sci. China Inf. Sci. 61(5), 050106 (2018)
Xu, W., Sun, X., Hu, J., Li, B.: Repersp: recommending personalized software projects on github. In: IEEE International Conference on Software Maintenance & Evolution (2017)
Xu, W., Sun, X., Xia, X., Chen, X.: Scalable relevant project recommendation on github. In: Proceedings of the 9th Asia-Pacific Symposium on Internetware, pp. 1–10 (2017)
Xu, Z., et al.: Tstss: a two-stage training subset selection framework for cross version defect prediction. J. Syst. Softw. 154, 59–78 (2019)
Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209. Melbourne, Australia (2017)
Yu, L., Mishra, A., Mishra, D.: An empirical study of the dynamics of github repository and its impact on distributed software development. In: OnTheMove (OTM 2014) (2014)
Zhang, L., Zou, Y., Xie, B., Zhu, Z.: Recommending relevant projects via user behaviour: an exploratory study on github. In: Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies, pp. 25–30 (2014)
Zhang, Y., Lo, D., Kochhar, P.S., Xia, X., Li, Q., Sun, J.: Detecting similar repositories on github. In: IEEE International Conference on Software Analysis (2017)
Acknowledgments
This study was supported by National Natural Science Foundation of China (NSFC): Research on service recommendation of trusted sharing and heterogeneous data fusion in the mobile crowd sensing environment (Grant no.62072060).
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Yang, H., Sun, S., Wen, J., Cai, H., Mateen, M. (2021). Improving Personalized Project Recommendation on GitHub Based on Deep Matrix Factorization. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_19
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DOI: https://doi.org/10.1007/978-3-030-92635-9_19
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