TY - GEN
T1 - Hierarchical clustering for collaborative filtering recommender systems
AU - Chalco, César Inga
AU - Chasi, Rodolfo Bojorque
AU - Ortiz, Remigio Hurtado
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Agglomerative hierarchical clustering
KW - Collaborative filtering
KW - Recommender systems
KW - Similarity metrics
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049689204&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85049689204&origin=inward
UR - http://www.mendeley.com/research/hierarchical-clustering-collaborative-filtering-recommender-systems
U2 - 10.1007/978-3-319-94229-2_34
DO - 10.1007/978-3-319-94229-2_34
M3 - Conference contribution
SN - 9783319942285
T3 - Advances in Intelligent Systems and Computing
SP - 346
EP - 356
BT - Hierarchical clustering for collaborative filtering recommender systems
A2 - Ahram, Tareq Z.
T2 - Advances in Intelligent Systems and Computing
Y2 - 1 January 2015
ER -