Enhanced Product Review Recommendations Using Collaborative Filtering and Singular Value Decomposition

Abstract

Recommender systems have become indispensable tools for enhancing user satisfaction and engagement across diverse business sectors, including online marketplaces, streaming services, and e-commerce platforms. This research proposes and evaluates an advanced product review recommendations system that leverages collaborative filtering techniques to deliver personalized and accurate suggestions. By integrating memory-based and model-based collaborative filtering approaches, the system effectively analyses user-item interactions to predict preferences. A key innovation of this study is the application of Singular Value Decomposition (SVD) to decompose the user-item matrix, which not only improves prediction efficiency but also reduces computational demands by addressing data sparsity and dimensionality challenges.

The system employs item-based collaborative filtering, utilizing the KNNWithMeans algorithm, and achieves a prediction accuracy of 1.34 RMSE, as validated through rigorous testing on a large-scale electronics product review dataset. Additionally, a correlation-based method is implemented to identify strongly associated products, enabling the generation of highly relevant recommendations. Experimental results demonstrate that the proposed framework outperforms existing recommendation models in terms of scalability and accuracy, particularly for large datasets.

Furthermore, this research explores the potential of hybrid models and deep learning techniques to further enhance recommendation quality and mitigate common issues such as the cold-start problem and data sparsity. The findings highlight the system’s robustness in real-world applications and its adaptability to dynamic user behaviour. By combining collaborative filtering with matrix factorization, this study provides a scalable and efficient solution for modern e-commerce platforms seeking to improve user experience and drive sales. Future directions include integrating real-time processing capabilities and exploring advanced machine learning algorithms to refine recommendation precision.

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