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Knowledge Discovery in a Recommender System: The Matrix Factorization Approach

Murchhana Tripathy

https://orcid.org/0000-0002-2376-5554

Information Systems & Technology, T A Pai Management Institute, Manipal Academy of Higher Education, Manipal, India

Department of Mathematics, ITER, Siksha ‘O’ Anusandhan Deemed to be University, Odisha, India

Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, AP, India

E-mail Address: murchhanatripathy@gmail.com

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Santilata Champati

Information Systems & Technology, T A Pai Management Institute, Manipal Academy of Higher Education, Manipal, India

Department of Mathematics, ITER, Siksha ‘O’ Anusandhan Deemed to be University, Odisha, India

Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, AP, India

E-mail Address: santilatachampati@soa.ac.in

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Hemanta Kumar Bhuyan

Information Systems & Technology, T A Pai Management Institute, Manipal Academy of Higher Education, Manipal, India

Department of Mathematics, ITER, Siksha ‘O’ Anusandhan Deemed to be University, Odisha, India

Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, AP, India

E-mail Address: hmb.bhuyan@gmail.com

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https://doi.org/10.1142/S0219649222500514Cited by:4 (Source: Crossref)

Abstract

Two famous matrix factorization techniques, the Singular Value Decomposition (SVD) and the Nonnegative Matrix Factorization (NMF), are popularly used by recommender system applications. Recommender system data matrices have many missing entries, and to make them suitable for factorization, the missing entries need to be filled. For matrix completion, we use mean, median and mode as three different cases of imputation. The natural clusters produced after factorization are used to formulate simple out-of-sample extension algorithms and methods to generate recommendation for a new user. Two cluster evaluation measures, Normalized Mutual Information (NMI) and Purity are used to evaluate the quality of clusters.

Keywords:

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