深度神经网络让推荐系统更懂用户偏好。
A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
- 用深层神经网络捕捉用户行为的复杂非线性关系。
- 对比传统方法，新模型在多个数据集上提升推荐准确率。
- 适合对推荐算法优化感兴趣的工程师与研究者。
本综述探讨了深度神经网络（DNN）在协同过滤（CF）推荐系统中的应用。随着数字世界日益依赖数据驱动方法，传统协同过滤技术面临可扩展性和灵活性不足的挑战。DNN 能通过有效建模数据中的复杂非线性关系解决这些问题。文章首先介绍协同过滤与深度神经网络的基本原理，为后续融合分析奠定基础。随后，系统回顾该领域的关键进展，分类梳理了多种增强型深度学习模型，包括多层感知机（MLP）、卷积神经网络（CNN）、循环神经网络（RNN）、图神经网络（GNN）、自编码器、生成对抗网络（GAN）和受限玻尔兹曼机（RBM）。论文还讨论了评估协议、公开可用的辅助信息及数据特征。最后，总结了当前面临的挑战与未来研究方向，旨在推动基于深度学习的协同过滤系统进一步发展。
原文摘要 · Abstract (English)
This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effectively modeling complex, non-linear relationships within the data. We begin by exploring the fundamental principles of both collaborative filtering and deep neural networks, laying the groundwork for understanding their integration. Subsequently, we review key advancements in the field, categorizing various deep learning models that enhance CF systems, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, Generative Adversarial Networks (GAN), and Restricted Boltzmann Machines (RBM). The paper also discusses evaluation protocols, various publicly available auxiliary information, and data features. Furthermore, the survey concludes with a discussion of the challenges and future research opportunities in enhancing collaborative filtering systems with deep learning.
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