ew 18: e14

Research Article

Matrix Factorization Based Recommendation System using Hybrid Optimization Technique

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  • @ARTICLE{10.4108/eai.19-2-2021.168725,
        author={P. Srinivasa Rao and T.V. Madhusudhana Rao and Suresh Kurumalla and Bethapudi Prakash},
        title={Matrix Factorization Based Recommendation System using Hybrid Optimization Technique},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2021},
        month={2},
        keywords={matrix factorization, ALS, SGD, optimization, recommendation system, latent factor, collaborative filtering},
        doi={10.4108/eai.19-2-2021.168725}
    }
    
  • P. Srinivasa Rao
    T.V. Madhusudhana Rao
    Suresh Kurumalla
    Bethapudi Prakash
    Year: 2021
    Matrix Factorization Based Recommendation System using Hybrid Optimization Technique
    EW
    EAI
    DOI: 10.4108/eai.19-2-2021.168725
P. Srinivasa Rao1,*, T.V. Madhusudhana Rao2, Suresh Kurumalla3, Bethapudi Prakash4
  • 1: Associate Professor of CSE, MVGR College of Engineering, Vizianagaram, Andhrapradesh, India
  • 2: Department of CSE, Vignan’s Institute of Information Technology, Visakhapatnam, India
  • 3: Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India
  • 4: Department of IT, Vignan's Institute of Engineering for Women, Visakhapatnam, India
*Contact email: psr.sri@gmail.com

Abstract

In this paper, a matrix factorization recommendation algorithm is used to recommend items to the user by inculcating a hybrid optimization technique that combines Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) in the advanced stage and compares the two individual algorithms with the hybrid model. This hybrid optimization algorithm can be easily implemented in the real world as a cold start can be easily reduced. The hybrid technique proposed is set side-by-side with the ALS and SGD algorithms individually to assess the pros and cons and the requirements to be met to choose a specific technique in a specific domain. The metric used for comparison and evaluation of this technique is Mean Squared Error (MSE).