Enhancing E-commerce Product Recommendations Using Hybrid Collaborative Filtering and Neural Network Algorithms

Abstract

This research paper explores the development and evaluation of an advanced recommendation system for e-commerce platforms by integrating hybrid collaborative filtering techniques with neural network algorithms. The study addresses the limitations of traditional collaborative filtering methods, such as sparsity and scalability issues, by combining them with neural networks' ability to model complex, non-linear relationships in large datasets. The proposed system employs a two-layer framework: the first layer utilizes matrix factorization to capture latent user-item interactions, while the second leverages deep neural networks to refine and personalize recommendations based on user behavior and contextual data. Experimental results, conducted on real-world e-commerce datasets, demonstrate that the hybrid model significantly outperforms conventional recommendation approaches in terms of accuracy, precision, and recall. Furthermore, the system's architecture supports dynamic adaptation to changing user preferences and diversification of recommendations, enhancing user satisfaction. This paper also discusses the computational efficiency of the proposed method, offering a viable solution for real-time application in large-scale e-commerce environments. The findings underscore the potential of integrating collaborative filtering with neural networks to revolutionize personalized e-commerce experiences, paving the way for future research in adaptive recommendation systems.