Abstract
This paper presents a methodology that combines latent factor models with graph-based models. The proposed recommendation system identifies a recommended item as a node of a graph. More specifically, the topology of the graph and the paths between the nodes are considered as critical features regarding the associations between them. Furthermore, in the current approach, these structural features are considered as feedback. These structural features are extracted from a pool of several application graphs which are afterwards generalized into a unified matrix of proximities. The main reason for the use of this structural feedback is to generate recommendations and discover unobserved relations using matrix factorization techniques. The approach is tested on a data set that consists of cloud-native microservices graphs.
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
References
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine learning, pp. 791–798. ACM, June 2007
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341. Springer, Heidelberg (2007)
Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Syst. 69(Suppl. 32), 175–186 (2000)
Gori, M., Pucci, A., Roma, V., Siena, I.: ItemRank: a random-walk based scoring algorithm for recommender engines. In: IJCAI, vol. 7, pp. 2766–2771, January 2007
Yang, J.H., Chen, C.M., Wang, C.J., Tsai, M.F.: HOP-rec: high-order proximity for implicit recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 140–144. ACM, September 2018
Park, H., Jung, J., Kang, U.: A comparative study of matrix factorization and random walk with restart in recommender systems. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 756–765. IEEE, December 2018
Skoutas, D., Sacharidis, D., Simitsis, A., Kantere, V., Sellis, T.: Top-k dominant web services under multi-criteria matching. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 898–909. ACM, March 2009
Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, vol. 8, pp. 263–272, December 2008
Zhou, D., Zhu, S., Yu, K., Song, X., Tseng, B.L., Zha, H., Giles, C.L.: Learning multiple graphs for document recommendations. In: Proceedings of the 17th International Conference on World Wide Web, pp. 141–150. ACM, April 2008
Zhou, D., Burges, C.J.: Spectral clustering and transductive learning with multiple views. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1159–1166. ACM, June 2007
Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: Fast and accurate link prediction in social networking systems. J. Syst. Softw. 85(9), 2119–2132 (2012)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
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. ACM, August 2008
JuJu © Canonical Ltd.: Juju: an open source application modelling tool that allows you to deploy, configure, scale and operate cloud infrastructures (2018). https://jujucharms.com/store
Hug, N.: Surprise, a Python library for recommender systems (2017). http://surpriselib.com
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 257–260. ACM, September 2010
Acknowledgment
This work has received funding from the European Union Horizon 2020 research and innovation program under Grant Agreement No. 761898 project MATILDA and under Grant Agreement No. 871643 project MORPHEMIC.
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
Tsoumas, I., Symvoulidis, C., Kyriazis, D. (2021). Learning a Generalized Matrix from Multi-graphs Topologies Towards Microservices Recommendations. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-55187-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55186-5
Online ISBN: 978-3-030-55187-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)