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Reducing the cold-user and cold-item problem in recommender system by reducing the Spectral representation of principal components in signals and images using G-lets decomposition of sub bands

Publication Type : Journal Article

Publisher : IEEE Region 10 Annual International Conference, Proceedings TENCON

Source : IEEE Region 10 Annual International Conference, Proceedings TENCON, pp. 3809 -3812., 2017.

ISBN : 9781509025978

Campus : Coimbatore

School : School of Engineering

Department : Computer Science, Chemical, Civil

Verified : No

Year : 2017

Abstract : In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

Cite this Research Publication : Dr. Rajathilagam B. and Dr. Murali Rangarajan, “Reducing the cold-user and cold-item problem in recommender system by reducing the Spectral representation of principal components in signals and images using G-lets decomposition of sub bands”, IEEE Region 10 Annual International Conference, Proceedings TENCON, pp. 3809 -3812., 2017.

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