Hierarchical clustering for collaborative filtering recommender systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish
Title of host publicationHierarchical clustering for collaborative filtering recommender systems
EditorsTareq Z. Ahram
Pages346-356
Number of pages11
ISBN (Electronic)9783319942285
DOIs
StatePublished - 1 Jan 2019
EventAdvances in Intelligent Systems and Computing - , Germany
Duration: 1 Jan 2015 → …

Publication series

NameAdvances in Intelligent Systems and Computing
Volume787
ISSN (Print)2194-5357

Conference

ConferenceAdvances in Intelligent Systems and Computing
Country/TerritoryGermany
Period1/01/15 → …

Keywords

  • Agglomerative hierarchical clustering
  • Collaborative filtering
  • Recommender systems
  • Similarity metrics

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