Deep Learning-Based Personalized Recommendation Using Customer Behavior and Purchase History in E-Commerce


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 6, pp. 237-244, Jun. 2022
https://doi.org/10.3745/KTSDE.2022.11.6.237,   PDF Download:
Keywords: Online Behavior Log, Purchase History, VAE-based Recommendation, Extracting Latent Space
Abstract

In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.


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Cite this article
[IEEE Style]
D. Y. Hong, G. Y. Kim, H. H. Kim, "Deep Learning-Based Personalized Recommendation Using Customer Behavior and Purchase History in E-Commerce," KIPS Transactions on Software and Data Engineering, vol. 11, no. 6, pp. 237-244, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.6.237.

[ACM Style]
Da Young Hong, Ga Yeong Kim, and Hyon Hee Kim. 2022. Deep Learning-Based Personalized Recommendation Using Customer Behavior and Purchase History in E-Commerce. KIPS Transactions on Software and Data Engineering, 11, 6, (2022), 237-244. DOI: https://doi.org/10.3745/KTSDE.2022.11.6.237.