Personalized product recommendations based on deep learning

Jiachang He

doi:10.54254/2755-2721/45/20241419

Research Article

Open access

Published on 15 March 2024

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He,J. (2024). Personalized product recommendations based on deep learning. Applied and Computational Engineering,45,259-263.

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Personalized product recommendations based on deep learning

Jiachang He *^,1,

¹ The Pennsylvania State University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/45/20241419

Abstract

Offline commodity sales efficiency is low, which brings a lot of inconvenience to people. Fortunately, with the progress of information technology, online shopping platforms have become popular. Upon accessing shopping platforms, myriad products spanning household appliances, groceries, apparel, and electronics become instantly available. The variety of goods on the online shopping platform can’t be directly contacted, which increases the difficulty of the user's choice of goods. In addition, it is difficult for online merchants to recommend suitable products to customers in the way of offline communication. To solve this problem, this paper proposes a product recommendation model based on deep learning. This model uses consumer preferences, historical shopping data, and product characteristics to calculate personalized recommendation scores. Such scores expediently guide consumers to products that align with their specific requirements, enhancing the overall purchasing experience. At the same time, it also improves the sales efficiency of merchants.

Keywords

Deep learning, Neural network, Product recommendation

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References

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Cite this article

He,J. (2024). Personalized product recommendations based on deep learning. Applied and Computational Engineering,45,259-263.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Conference website: https://www.confspml.org/

ISBN：978-1-83558-331-9(Print) / 978-1-83558-332-6(Online)

Conference date: 15 January 2024

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.45

ISSN：2755-2721(Print) / 2755-273X(Online)

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