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

Author

Listed:
  • Murchhana Tripathy
  • Santilata Champati
  • Srikanta Patnaik

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.

Suggested Citation

  • Murchhana Tripathy & Santilata Champati & Srikanta Patnaik, 2022. "SVD-initialised K-means clustering for collaborative filtering recommender systems," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 21(1), pages 71-91.
    • Handle: RePEc:ids:ijmdma:v:21:y:2022:i:1:p:71-91
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