Skip to main content

A comparative analysis of similarity metrics on sparse data for clustering in recommender systems

2Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This work shows similarity metrics behavior on sparse data for recommender systems (RS). Clustering in RS is an important technique to perform groups of users or items with the purpose of personalization and optimization recommendations. The majority of clustering techniques try to minimize the Euclidean distance between the samples and their centroid, but this technique has a drawback on sparse data because it considers the lack of value as zero. We propose a comparative analysis of similarity metrics like Pearson Correlation, Jaccard, Mean Square Difference, Jaccard Mean Square Difference and Mean Jaccard Difference as an alternative method to Euclidean distance, our work shows results for FilmTrust and MovieLens 100K datasets, these both free and public with high sparsity. We probe that using similarity measures is better for accuracy in terms of Mean Absolute Error and Within-Cluster on sparse data.

Cite

CITATION STYLE

APA

Bojorque, R., Hurtado, R., & Inga, A. (2019). A comparative analysis of similarity metrics on sparse data for clustering in recommender systems. In Advances in Intelligent Systems and Computing (Vol. 787, pp. 291–299). Springer Verlag. https://doi.org/10.1007/978-3-319-94229-2_28

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free