Benchmarking Deep Neural Networks for Modern Recommendation Systems

This paper presents a requirement-oriented benchmark of seven deep neural architectures, CNN, RNN, GNN, Autoencoder, Transformer, Neural Collaborative Filtering, and Siamese Networks, across three real-world datasets: Retail E-commerce, Amazon Products, and Netflix Prize. To ensure a fair and comprehensive comparison aligned with the evolving demands of modern recommendation systems, we adopt a Requirement-Oriented Benchmarking (ROB) framework that structures evaluation around predictive accuracy, recommendation diversity, relational awareness, temporal dynamics, and computational efficiency. Under a unified evaluation protocol, models are assessed using standard accuracy-oriented metrics alongside diversity and efficiency indicators. Experimental results show that different architectures exhibit complementary strengths across requirements, motivating the use of hybrid and ensemble designs. The findings provide practical guidance for selecting and combining neural architectures to better satisfy multi-objective recommendation system requirements.