Clustering-Based Recommender System: Bundle Recommendation Using Matrix Factorization to Single User and User Communities

  • Remigio Hurtado OrtizEmail author
  • Rodolfo Bojorque Chasi
  • César Inga Chalco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 787)


This paper shows the results of a Recommender System (RS) that suggests bundles of items to a user or a community of users. Nowadays, there are several RS that realize suggestions of a unique item considering the preferences of a user. However, these RS are not scalable and sometimes the suggestions that make are far from a user’s preferences. We propose an RS that suggests bundles of items to one user or a community of users with similar affinities. This RS uses an algorithm based on Matrix Factorization (MF). To execute the experiments, we use released databases with high dispersion. The results obtained are evaluated per the metrics Accuracy, Precision, Recall and F-measure. The results demonstrate that the proposed method improves significantly the quality of the suggestions.


Recommender System Bundles of items Matrix Factorization 


  1. 1.
    Ortega, F., Hernando, A., Bobadilla, J., Kang, J.H.: Recommending items to group of users using Matrix Factorization based Collaborative Filtering. Inf. Sci. 345, 313–324 (2016)CrossRefGoogle Scholar
  2. 2.
    Arthur, D., Vassilvitskii, S.: K-means ++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium Discrete Algorithms, pp. 1027–1035 (2007)Google Scholar
  3. 3.
    Zahra, S., Ghazanfar, M.A., Khalid, A., Azam, M.A., Naeem, U., Prugel-Bennett, A.: Novel centroid selection approaches for K-means-clustering based recommender systems. Inf. Sci. (Ny) 320, 156–189 (2015)CrossRefGoogle Scholar
  4. 4.
    Ortega, J.P., del Rocio, M., Rojas, B., Somodevilla Garcia, M.J.: Research issues on K-means algorithm: an experimental trial using matlab. In: Proceedings 2nd Workshop Semantic Web New Technology, pp. 83–96 (2009)Google Scholar
  5. 5.
    Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds.) Information Retrieval and Mining in Distributed Environments, pp. 1–20. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Mohankumar, R., Saravanan, D.: Design of quality-based recommender system for bundle purchases. In: National Conference on Recent Trends in Communication on Engineering Tamil Nadu College, Coimbatore-641 659, vol. 1, pp. 1–8 (2014)Google Scholar
  7. 7.
    Casimiro, K.H.: Diseño de un sistema de recomendación turístico para Cd. Juárez, Chih., México Karina Hernández CasimiroGoogle Scholar
  8. 8.
    Silvia, S.: ‘Un enfoque para Sistemas de Recomendación de paquetes turísticos basado en restricciones de usuario.’ Trabajo final de Ingeniería de Sistemas (2016)Google Scholar
  9. 9.
    Bojorque, R., Hurtado, R.: Técnicas híbridas en Sistemas de Recomendación para optimizar el Modelo Non Negative Matrix Factorization. Universidad Politécnica de Madrid (2017)Google Scholar
  10. 10.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc: Ser. C (Appl. Stat.) 28(1), 100–108 (1979). Scholar
  11. 11.
    Zhu, T., Harrington, P., Li, J., Tang, L.: Bundle recommendation in eCommerce. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 657–666 (2014)Google Scholar
  12. 12.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  13. 13.
    Bobadilla, J., Bojorque, R., Hernando, A., Hurtado, R.: Recommender systems clustering using Bayesian non negative matrix factorization. IEEE Access 6, 1 (2018)CrossRefGoogle Scholar
  14. 14.
    Hernando, A., Bobadilla, J., Ortega, F.: A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl.-Based Syst. 97, 188–202 (2016)CrossRefGoogle Scholar
  15. 15.
    Hernández del Olmo, F., Gaudioso, E.: Evaluation of recommender systems: a new approach. Expert Syst. Appl. 35(3), 790–804 (2008)CrossRefGoogle Scholar
  16. 16.
    Pham, M.C., Cao, Y., Klamma, R., Jarke, M.: A clustering approach for collaborative filtering recommendation using social network analysis. J. Univers. Comput. Sci. 17(4), 1–21 (2011)Google Scholar
  17. 17.
    Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook, 1003 p. Springer+Business Media, New York (2015). ISBN 978-1-4899-7636-9CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Remigio Hurtado Ortiz
    • 1
    • 2
    Email author
  • Rodolfo Bojorque Chasi
    • 1
    • 2
  • César Inga Chalco
    • 1
  1. 1.Universidad Politécnica SalesianaCuencaEcuador
  2. 2.Universidad Politécnica de MadridMadridSpain

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