Ensemble Deep Learning Models for Collaborative Filtering Recommendations

Authors

  • Gangolu Yedukondalu Professor, CSE (AI&ML) Department, Vignana Bharathi Institute of Technology, Hyderabad ,Telangana. India.
  • Jyothirmai Joshi Assistant professor, VNR Vignana Jyothi institute of engineering and technology
  • Krishan Dev Nidumolu MSCSIA, Gildart Haase School of Computer Sciences and Engineering Fairleigh Dickinson University
  • Adapa Gopi Associate Professor, Department: CSE, Koneru Lakshmaiah Education Foundation
  • G. Rajavikram Professor , Department of Computer Science And Engineering, Vignan Institute of Technology and Science, Telangana, India.

Keywords:

Collaborative Filtering, Deep Learning, Multiple Models, Ensemble Approach, Context-aware Recommendations, Transfer Learning

Abstract

This paper introduces a deep learning approach that uses multiple models to enhance the accuracy and diversity of collaborative filtering recommendation systems. The approach is based on deep neural networks and an ensemble approach is used to combine predictions from different models. However, the proposed approach faces several challenges that need to be addressed to make it practical and effective. The main challenges include data sparsity, the cold start problem, model complexity, interpretability, and scalability. Techniques are required to handle sparse data, while the cold start problem can be addressed by utilizing content-based filtering or auxiliary data sources. Addressing the issues of model complexity and interpretability is also important, as complex models may lead to overfitting and poor generalization, while black-box models may not be easily understood by users. Finally, the approach must be scalable to handle large datasets and high-dimensional feature spaces. In conclusion, the proposed deep learning approach holds promise for improving recommendation system performance. However, the challenges and issues associated with the approach must be addressed to realize its full potential. Developing effective techniques to handle sparse data and the cold start problem, simplifying model complexity, improving interpretability, and ensuring scalability are all important steps in making the approach practical and effective in real-world settings.

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References

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Flow model of the proposed system

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Published

17.05.2023

How to Cite

Yedukondalu , G. ., Joshi , J. ., Nidumolu , K. D. ., Gopi , A. ., & Rajavikram, G. . (2023). Ensemble Deep Learning Models for Collaborative Filtering Recommendations. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 358–369. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2862

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Research Article

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