Collaborative Filtering Based Hybrid Recommendation System Using Neural Network and Matrix Factorization Techniques

Authors

  • Krishan Kant Yadav Sr. Assistant Professor & Research Scholar, Department of Computer Science & Engineering, Amity University, Gwalior, Madhya Pradesh, India.
  • Hemant Kumar Soni Associate Professor, Department of Computer Science & Engineering, Amity University, Gwalior, Madhya Pradesh, India.
  • Ghanshyam Yadav Assistant Professor, IATS Engineering College, Greater Noida, Uttar Pradesh, India.
  • Mamta Sharma Assistant Professor, Department of Mathematics and Computer Science, Prestige Institute of Management & Research, Gwalior, M.P, India.

Keywords:

Collaborative based filtering, Content based filtering, Coverage, Matrix factorization, Neural networks, Hybrid Recommendation system

Abstract

This research paper presents a novel work on collaborative filtering based hybrid recommendation system. A hybrid recommendation system is a best combination of content based filtering and collaborative based filtering recommendation systems. In recent years, recommendation systems have become an essential part of our daily lives, assisting us in making informed decisions about what to buy, read, watch, and listen to. Collaborative filtering (CF) and matrix factorization (MF) are widely used techniques for building recommendation systems. However, they suffer from certain constraints, such as the cold-start problem, sparsity, and scalability. Hybrid recommendation systems combine multiple recommendation algorithms to overcome individual algorithms' limitations and improve recommendations' accuracy and coverage. In our next contribution, we have suggested a hybrid recommendation system to enhance the accuracy and coverage of suggestions by combining MF with NN. On the other hand, deep learning-based approaches such as neural networks (NN) have shown great promise in overcoming these limitations. In this research, we propose a novel hybrid recommendation system that combines the strengths of MF and NN to improve the accuracy and diversity of recommendations. We evaluate the proposed method on three popular datasets MovieLense, Hind Movie and Book Crossing and compare its performance with other state-of-the-art recommendation algorithms. The results demonstrate that the proposed hybrid approach outperforms the individual MF and NN models and achieves better coverage with the lowest Root Mean Squared Error (RMSE).

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References

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Published

13.12.2023

How to Cite

Yadav, K. K. ., Soni, H. K. ., Yadav, G. ., & Sharma, M. . (2023). Collaborative Filtering Based Hybrid Recommendation System Using Neural Network and Matrix Factorization Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 695–701. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4307

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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