对比七种神经网络在推荐系统中的表现，找出各模型优劣。
Benchmarking Deep Neural Networks for Modern Recommendation Systems
- 按预测精度、多样性等五大需求统一评测
- 不同模型在各项指标上各有优势
- 适合需要多目标优化的推荐系统设计
本文针对现代推荐系统的需求，对七种深度神经架构（CNN、RNN、GNN、Autoencoder、Transformer、Neural Collaborative Filtering、Siamese Networks）在Retail E-commerce、Amazon Products、Netflix Prize三个真实数据集上进行需求导向的基准测试。采用需求导向基准框架（ROB），从预测准确率、推荐多样性、关系感知、时序动态性及计算效率五个维度评估模型。在统一评估协议下，使用标准准确率指标以及多样性和效率指标进行综合评价。实验结果表明，不同架构在各项需求上表现出互补优势，支持混合与集成设计。研究为满足多目标推荐系统需求提供了实际选型与组合建议。
原文摘要 · Abstract (English)
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.
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