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http://dx.doi.org/10.3745/KTSDE.2022.11.6.237

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

Hong, Da Young (고려대학교 의학통계학협동과정)
Kim, Ga Yeong (성균관대학교 인공지능학과)
Kim, Hyon Hee (동덕여자대학교 정보통계학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.6, 2022 , pp. 237-244 More about this Journal
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.
Keywords
Online Behavior Log; Purchase History; VAE-based Recommendation; Extracting Latent Space;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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