Version 1
: Received: 14 October 2024 / Approved: 15 October 2024 / Online: 15 October 2024 (08:48:30 CEST)
How to cite:
Zhao, W. Research on the Application of Deep Neural Networks in Personalized Product Recommendations on E-Commerce Platforms. Preprints2024, 2024101167. https://doi.org/10.20944/preprints202410.1167.v1
Zhao, W. Research on the Application of Deep Neural Networks in Personalized Product Recommendations on E-Commerce Platforms. Preprints 2024, 2024101167. https://doi.org/10.20944/preprints202410.1167.v1
Zhao, W. Research on the Application of Deep Neural Networks in Personalized Product Recommendations on E-Commerce Platforms. Preprints2024, 2024101167. https://doi.org/10.20944/preprints202410.1167.v1
APA Style
Zhao, W. (2024). Research on the Application of Deep Neural Networks in Personalized Product Recommendations on E-Commerce Platforms. Preprints. https://doi.org/10.20944/preprints202410.1167.v1
Chicago/Turabian Style
Zhao, W. 2024 "Research on the Application of Deep Neural Networks in Personalized Product Recommendations on E-Commerce Platforms" Preprints. https://doi.org/10.20944/preprints202410.1167.v1
Abstract
Due to the influence of factors such as user consumption habits and manufacturer service quality, e-commerce platforms cannot push personalized product information to any consumer. This project studies the push of product information on e-commerce platforms based on deep neural networks. It mines the data of users' online shopping behaviors such as browsing, collecting, and adding to cart on e-commerce platforms, and pre-processes the mined user behavior data by cleaning, integrating, and normalizing them. It builds a deep bidirectional Transformer model to learn users' historical behavior data and predict the most probable products that meet users' behavior needs, thereby realizing the automatic push of product information on e-commerce platforms. The experimental results show that the F1 value of the push result of product information on e-commerce platforms under the method designed by this algorithm is 0.97, which confirms the effectiveness and superiority of this method.
Keywords
Deep neural network; E-commerce platform; Personalization; Product information push
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
