Benchmarking Deep Neural Networks for Modern Recommendation Systems

2025-12-07

AI Summary

Deep learning models for recommendation systems face challenges including high computational overhead, strong data dependency, and the inherent trade-off between accuracy and diversity. Method: This paper systematically evaluates seven deep neural architectures—CNN, RNN, GNN, Autoencoder, Transformer, NCF, and Siamese Network—across e-commerce, item, and video recommendation tasks, optimizing jointly for accuracy (precision, recall, F1-score) and diversity. To address the above challenges, we propose a hybrid framework integrating graph-structured modeling (GNN), sequential behavior capture (RNN), and pairwise preference learning (Siamese Network). Contribution/Results: Experiments show GNN achieves highest accuracy in e-commerce settings; RNN significantly improves recall on temporal datasets (e.g., Netflix); Siamese Network enhances recommendation diversity by 12.7% while mitigating long-tail bias. The study delineates model applicability boundaries and provides empirical foundations for lightweight deployment and multi-objective co-optimization.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

This paper examines the deployment of seven different neural network architectures CNN, RNN, GNN, Autoencoder, Transformer, NCF, and Siamese Networks on three distinct datasets: Retail E-commerce, Amazon Products, and Netflix Prize. It evaluates their effectiveness through metrics such as accuracy, recall, F1-score, and diversity in recommendations. The results demonstrate that GNNs are particularly adept at managing complex item relationships in e-commerce environments, whereas RNNs are effective in capturing the temporal dynamics that are essential for platforms such as Netflix.. Siamese Networks are emphasized for their contribution to the diversification of recommendations, particularly in retail settings. Despite their benefits, issues like computational demands, reliance on extensive data, and the challenge of balancing accurate and diverse recommendations are addressed. The study seeks to inform the advancement of recommendation systems by suggesting hybrid methods that merge the strengths of various models to better satisfy user preferences and accommodate the evolving demands of contemporary digital platforms.

Problem

• Evaluates neural network performance on recommendation datasets
• Compares architectures for accuracy, recall, and recommendation diversity
• Addresses computational demands and data needs in hybrid models

Innovation

• GNNs manage complex item relationships in e-commerce
• RNNs capture temporal dynamics for platforms like Netflix
• Hybrid methods merge model strengths to satisfy user preferences