Hierarchical Clustering for Collaborative Filtering Recommender Systems

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


Nowadays, the Recommender Systems (RS) that use Collaborative Filtering (CF) are objects of interest and development. CF allows RS to have a scalable filtering, vary metrics to determine the similarity between users and obtain very precise recommendations when using dispersed data. This paper proposes an RS based in Agglomerative Hierarchical Clustering (HAC) for CF. The databases used for the experiments are released and of high dispersion. We used five HAC methods in order to identify which method provides the best results, we also analyzed similarity metrics such as Pearson Correlation (PC) and Jaccard Mean Square Difference (JMSD) versus Euclidean distance. Finally, we evaluated the results of the proposed algorithm through precision, recall and accuracy.


Recommender Systems Collaborative Filtering Agglomerative Hierarchical Clustering Similarity metrics 


  1. 1.
    Galán, S.M.: Filtrado Colaborativo y Sistemas de Recomendación, IRC 2007, Univ. Carlos III Madrid, pp. 1–8 (2007)Google Scholar
  2. 2.
    Zinke, C., Meyer, K., Friedrich, J., Reif, L.: Digital social learning – collaboration and learning in enterprise social networks, vol. 596, pp. 3–11 (2018)Google Scholar
  3. 3.
    Covington, M.J., Carskadden, R.: Threat implications of the internet of things. In: 2013 5th International Conference Cyber Conflict, pp. 1–12 (2013)Google Scholar
  4. 4.
    Goebel, R.: Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science LNAI Series Editors (2012)Google Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  7. 7.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations, no. July (2002)Google Scholar
  8. 8.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136 : A K-Means Clustering Algorithm. J. Roy. Stat. Soc. Ser. C Appl. Stat. 28(1), 100–108 (2016). Published by : Wiley for the Royal Statistical Society Stable
  9. 9.
    Ortega, J.P., del Rocio Boone Rojas, M., Somodevilla Garcia, M.J.: Research issues on K-means algorithm : an experimental trial using matlab. In: Proceedings of 2nd Working Semantic Web New Technologies, pp. 83–96 (2009)Google Scholar
  10. 10.
    Fränti, P., Virmajoki, O., Hautamäki, V.: Fast agglomerative clustering using a k-nearest neighbor graph. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1875–1881 (2006)CrossRefGoogle Scholar
  11. 11.
    Menon, A.K., Chitrapura, K.-P., Garg, S., Agarwal, D., Kota, N.: Response prediction using collaborative filtering with hierarchies and side-information. In: Proceedings of 17th ACM SIGKDD International Conference Knowledge Discovery Data Mining - KDD 2011, p. 141 (2011)Google Scholar
  12. 12.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends® Hum.–Comput. Interact. 4(2), 10–14 (2011)Google Scholar
  13. 13.
    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
  14. 14.
    Müllner, D.: Modern hierarchical, agglomerative clustering algorithms, no. 1973, pp. 1–29 (2011)Google Scholar
  15. 15.
    Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of 2016 Conference North American Chapter Association Computational Linguistics Human Language Technologies, pp. 1480–1489 (2016)Google Scholar
  16. 16.
    Murtagh, F., Legendre, P.: Ward’s Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm, no. June, pp. 1–20 (2011)Google Scholar
  17. 17.
    Jamain, A., Hand, D.: Mining supervised classification performance studies: a meta-analytic investigation. J. Classif. 112, 87–112 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Zahra, S., Ghazanfar, M.A., Khalid, A., Azam, M.A., Naeem, U., Prugel-Bennett, A.: Novel centroid selection approaches for KMeans-clustering based recommender systems. Inf. Sci. (Ny) 320, 156–189 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Hernández del Olmo, F., Gaudioso, E.: Evaluation of recommender systems: a new approach. Expert Syst. Appl. 35(3), 790–804 (2008)CrossRefGoogle Scholar
  21. 21.
    Ricci, F.: Recommender Systems Handbook, 1003 p. Springer Science+Business Media, New York (2015). ISBN 978-1-4899-7636-9. Ricci, F., Rokach, L., Shapira, B. (eds.)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • César Inga Chalco
    • 1
    Email author
  • Rodolfo Bojorque Chasi
    • 1
    • 2
  • Remigio Hurtado Ortiz
    • 1
    • 2
  1. 1.Carrera de Ingeniería de Sistemas, Universidad Politécnica Salesiana del EcuadorCuencaEcuador
  2. 2.Universidad Politécnica de MadridMadridSpain

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