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International Conference on Computational Intelligence, Communications, and Business Analytics

CICBA 2017: Computational Intelligence, Communications, and Business Analytics pp 504–516Cite as

Genetic Algorithm-Based Matrix Factorization for Missing Value Prediction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

Sparsity is a major problem in the areas like data mining and pattern recognition. In recommender systems, predictions based on these few observations lead to avoidance of inherent latent features of the user corresponding to the item. Similarly, in different crowdsourcing based opinion aggregation models, there is a minimal chance to obtain opinions from all the crowd workers. Even this sparsity problem has an extensive effect in predicting actual rating of a particular item due to limited and incomplete observations. To deal with this issue, in this article, a genetic algorithm based matrix factorization technique is proposed to estimate the missing entries in the response matrix that contains workers’ responses over some questions. We have created three synthetic datasets and used one real-life dataset to show the efficacy of the proposed method over the other state-of-the-art approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Nadia, 741235, India

    Sujoy Chatterjee & Anirban Mukhopadhyay

Authors
  1. Sujoy Chatterjee
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  2. Anirban Mukhopadhyay
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Corresponding author

Correspondence to Sujoy Chatterjee .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Chatterjee, S., Mukhopadhyay, A. (2017). Genetic Algorithm-Based Matrix Factorization for Missing Value Prediction. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_39

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  • DOI: https://doi.org/10.1007/978-981-10-6430-2_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

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