Abstract
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.
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Galán, S.M.: Filtrado Colaborativo y Sistemas de Recomendación, IRC 2007, Univ. Carlos III Madrid, pp. 1–8 (2007)
Zinke, C., Meyer, K., Friedrich, J., Reif, L.: Digital social learning – collaboration and learning in enterprise social networks, vol. 596, pp. 3–11 (2018)
Covington, M.J., Carskadden, R.: Threat implications of the internet of things. In: 2013 5th International Conference Cyber Conflict, pp. 1–12 (2013)
Goebel, R.: Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science LNAI Series Editors (2012)
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)
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)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations, no. July (2002)
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). http://www.jstor.org/stable/2346830. Published by : Wiley for the Royal Statistical Society Stable
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)
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)
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)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends® Hum.–Comput. Interact. 4(2), 10–14 (2011)
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)
Müllner, D.: Modern hierarchical, agglomerative clustering algorithms, no. 1973, pp. 1–29 (2011)
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)
Murtagh, F., Legendre, P.: Ward’s Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm, no. June, pp. 1–20 (2011)
Jamain, A., Hand, D.: Mining supervised classification performance studies: a meta-analytic investigation. J. Classif. 112, 87–112 (2008)
Bojorque, R., Hurtado, R.: Técnicas híbridas en Sistemas de Recomendación para optimizar el Modelo Non Negative Matrix Factorization. Universidad Politécnica de Madrid (2017)
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)
Hernández del Olmo, F., Gaudioso, E.: Evaluation of recommender systems: a new approach. Expert Syst. Appl. 35(3), 790–804 (2008)
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.)
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Chalco, C.I., Chasi, R.B., Ortiz, R.H. (2019). Hierarchical Clustering for Collaborative Filtering Recommender Systems. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_34
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DOI: https://doi.org/10.1007/978-3-319-94229-2_34
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