A Brief Review of Basic Deep Learning Models for Recommendation Systems

Xitong Zhou

2024

Abstract

Recommendation systems are essential for delivering personalized content to users across various platforms, enhancing user experience and engagement. Traditional filtering methods, including content-based filtering and collaborative filtering, have been widely applied to recommend items based on user preferences or similarities between users and items. However, these methods still face challenges such as data sparsity, computational complexity, and the cold-start problem, which limit their effectiveness and scalability. This paper provides an overview of these traditional recommendation techniques, their limitations, and how deep learning approaches are transforming the field by addressing these issues. The discussion focuses on several deep learning models, including Multi-Layer Perceptrons (MLP), Autoencoders, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). These models enhance recommendation systems by capturing complex, non-linear interactions between users and items, thereby significantly improving personalization, scalability, and robustness in cold-start scenarios. By leveraging the power of neural networks, deep learning is ushering in a new era for recommendation systems, offering more accurate, dynamic, and adaptive recommendations.

Paper Citation

in Harvard Style

Zhou X. (2024). A Brief Review of Basic Deep Learning Models for Recommendation Systems. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 473-482. DOI: 10.5220/0013526300004619

in Bibtex Style

@conference{daml24,
author={Xitong Zhou},
title={A Brief Review of Basic Deep Learning Models for Recommendation Systems},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={473-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013526300004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - A Brief Review of Basic Deep Learning Models for Recommendation Systems
SN - 978-989-758-754-2
AU - Zhou X.
PY - 2024
SP - 473
EP - 482
DO - 10.5220/0013526300004619
PB - SciTePress