Intelligent Recommendation System Using Collaborative Filtering and Deep Learning

Abstract

Recommendation systems are essential for filtering vast amounts of digital information and delivering personalized content. However, conventional collaborative filtering techniques often face challenges such as data sparsity, cold-start issues, and limited scalability. This study proposes a hybrid recommendation approach that integrates collaborative filtering with a deep learning to address these limitations. The model uses neural collaborative filtering with embedding layers and multilayer perceptrons to capture non-linear user-item interaction, while matrix factorization and neighborhood- based methods help extract both explicit and implicit feedback. Experimental results on benchmark datasets show that the hybrid model achieves better performance than traditional methods, improving key metrics such as precision, recall, and nDCG. The findings suggest that combining deep learning with conventional recommendation techniques enhances accuracy, handles sparsity more effectively, and improves personalization for new users and items.

Introduction

The text discusses the development of a hybrid recommendation system that combines traditional collaborative filtering techniques with deep learning methods to improve personalized recommendations. Recommendation systems are widely used in platforms like Amazon, Netflix, Spotify, and YouTube to help users discover relevant products and content. Traditional approaches such as content-based and collaborative filtering face challenges including cold start, data sparsity, and scalability. To address these limitations, the study proposes integrating Neural Collaborative Filtering (NCF), matrix factorization, and neighborhood-based methods with deep learning techniques.

The proposed system uses implicit feedback data such as purchases, clicks, and browsing behavior to learn user preferences. Deep learning models with embeddings, autoencoders, and attention mechanisms improve the ability to capture complex user-item relationships and latent behavioral patterns. The dataset used is an e-commerce transactional dataset containing around 10,000 records with customer IDs, product IDs, quantities, and timestamps. Data preprocessing includes cleaning missing values, encoding categorical data, generating interaction matrices, and feature engineering.

The architecture combines Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) components to model both linear and non-linear interactions between users and items. The model is trained using binary cross-entropy loss and the Adam optimizer, with evaluation metrics including RMSE, precision, recall, and NDCG.

The study concludes that hybrid deep learning recommendation systems provide better personalization, scalability, robustness, and recommendation accuracy compared to traditional methods. By leveraging latent feature learning and implicit feedback, the proposed system effectively handles sparse datasets and dynamic user behavior, making it suitable for large-scale real-world recommendation platforms.

Conclusion

This study demonstrates the effectiveness of Neural Collaborative Filtering (NCF) in enhancing personalized product recommendations in e-commerce platforms. By integrating Generalized Matrix Factorization with Multi-Layer Perceptrons, the hybrid architecture successfully captures both linear and non-linear user–item interaction patterns. Experimental results confirm that the proposed NCF model provides superior predictive accuracy, robust implicit feedback processing, and improved generalization compared to traditional collaborative filtering methods. The model exhibits strong performance even under sparsity conditions, highlighting its ability to uncover latent preference structures and deliver highly personalized recommendations. Furthermore, the incorporation of embedding-based learning significantly improves representation quality, enabling the system to better interpret user behavior and item attributes. The results also reveal that neural network optimization and hybrid model design are essential for addressing core challenges such as data sparsity, scalability, and cold-start scenarios in real- world recommendation environments. In summary, the proposed system demonstrates promising potential for deployment in large-scale e-commerce platforms by offering accurate, scalable, and adaptive recommendations. Future work could explore enhancements through attention- based architectures, graph neural networks, and the incorporation of contextual and multimodal data to further improve recommendation diversity, interpretability, and real- time performance.

References

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Copyright

Copyright © 2026 Dr. Sajja Suneel, Ravali Tallapelly , Repakula Sai Varshini, Md Rameex Hussain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.