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.

翻译：本文针对现代推荐系统不断演进的需求，提出了一个需求导向的基准测试框架，对七种深度神经网络架构——CNN、RNN、GNN、自编码器、Transformer、神经协同过滤以及孪生网络——在三个真实世界数据集（零售电商、亚马逊商品和Netflix Prize）上进行了全面评估。为确保公平且全面的比较，我们采用了需求导向基准测试框架，围绕预测准确性、推荐多样性、关系感知能力、时序动态性和计算效率五个维度构建评估体系。在统一的评估协议下，各模型通过标准准确性指标以及多样性和效率指标进行评估。实验结果表明，不同架构在不同需求维度上展现出互补优势，这为采用混合与集成设计提供了依据。研究结果为选择和组合神经网络架构以更好地满足多目标推荐系统需求提供了实践指导。