sis 22(2): e2

Research Article

EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System

Download320 downloads
  • @ARTICLE{10.4108/eetsis.vi.1947,
        author={Asha K N and R Rajkumar},
        title={EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={7},
        keywords={Recommender system, machine learning, PCA, GA, IoT, Signal Strength, deep reinforcement learning, digital technology, Segmentation, Clustering},
        doi={10.4108/eetsis.vi.1947}
    }
    
  • Asha K N
    R Rajkumar
    Year: 2022
    EM_GA-RS: Expectation Maximization and GA-based Movie Recommender System
    SIS
    EAI
    DOI: 10.4108/eetsis.vi.1947
Asha K N1, R Rajkumar1,*
  • 1: Vellore Institute of Technology University
*Contact email: vitrajkumar@gmail.com

Abstract

This work introduced a novel approach for the movie recommender system using a machine learning approach. This work introduces a clustering-based approach to introduce a recommender system (RS). The conventional clustering approaches suffer from the clustering error issue, which leads to degraded performance. Hence, to overcome this issue, we developed an expectation- maximization-based clustering approach. However, due to imbalanced data, the performance of RS is degraded due to multicollinearity issues. Hence, we Incorporate PCA (Principal Component Analysis) based dimensionality reduction model to improve the performance. Finally, we aim to reduce the error; thus, a Genetic Algorithm (GA) is included to achieve the optimal clusters and assign the suitable recommendation. The experimental study is carried out on publically available movie datasets performance of the proposed approach is measured in terms of MSE (Mean Squared Error) and Root Mean Squared Error (RMSE). The comparative study shows that the proposed approach achieves better performance when compared with a state-of-art movie recommendation system.