Hierarchical clustering for collaborative filtering recommender systems

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2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaHierarchical clustering for collaborative filtering recommender systems
EditoresTareq Z. Ahram
Páginas346-356
Número de páginas11
ISBN (versión digital)9783319942285
DOI
EstadoPublicada - 1 ene. 2019
EventoAdvances in Intelligent Systems and Computing - , Alemania
Duración: 1 ene. 2015 → …

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen787
ISSN (versión impresa)2194-5357

Conferencia

ConferenciaAdvances in Intelligent Systems and Computing
País/TerritorioAlemania
Período1/01/15 → …

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