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An Evolutionary Matrix Factorization Approach for Missing Value Prediction

  • Sujoy Chatterjee
  • Anirban Mukhopadhyay
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 687)

Abstract

Sparseness of data is a common problem in many fields such as data mining and pattern recognition. During the last decade, collecting opinions from people has been established to be an useful tool for solving different real-life problems. In crowdsourcing systems, prediction based on very few observations leads to complete disregard for the inherent latent features of the crowd workers corresponding to the items. Similarly in bioinformatics, sparsity has a major negative impact in finding relevant gene from gene expression data. Although this problem is being studied over the last decade, there are some benefits and pitfalls of the different proposed approaches. In this article, we have proposed a genetic algorithm-based matrix factorization technique to estimate the missing entries in the rating matrix of recommender systems. We have created four synthetic datasets and used two real-life gene expression datasets to show the efficacy of the proposed method in comparison with the other state-of-the-art approaches.

Keywords

Matrix factorization Sparsity Genetic algorithm Judgment analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringUniversity of KalyaniNadiaIndia

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