SVD-initialised K-means clustering for collaborative filtering recommender systems

Murchhana Tripathy, Santilata Champati and Srikanta Patnaik

International Journal of Management and Decision Making, 2022, vol. 21, issue 1, 71-91

Abstract: K-means is a popular partitional clustering algorithm used by collaborative filtering recommender systems. However, the clustering quality depends on the value of K and the initial centroid points and consequently research efforts have instituted many new methods and algorithms to address this problem. Singular value decomposition (SVD) is a popular matrix factorisation technique that can discover natural clusters in a data matrix. We use this potential of SVD to solve the K-means initialisation problem. After finding the clusters, they are further refined by using the rank of the matrix and the within-cluster distance. The use of SVD based initialisation for K-means helps to retain the cluster quality and the cluster initialisation process gets automated.

Keywords: recommender systems; collaborative filtering; singular value decomposition; SVD; K-means initialisation; within-cluster distance; rank of the matrix. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=119580 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmdma:v:21:y:2022:i:1:p:71-91

Access Statistics for this article

More articles in International Journal of Management and Decision Making from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().