Personalized Size Recommender System for Online Apparel Shopping: A Collaborative Filtering Approach (온라인 의류 쇼핑을 위한 맞춤형 치수 추천 시스템: 협업 필터링 접근법)

Dongwon Lee (이동원)

doi:10.9708/jksci.2023.28.08.039

Personalized Size Recommender System for Online Apparel Shopping: A Collaborative Filtering Approach

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(8), pp.39-48
DOI : 10.9708/jksci.2023.28.08.039
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 19, 2023
Accepted : August 8, 2023
Published : August 31, 2023

Dongwon Lee ¹

¹한성대학교

Accredited

ABSTRACT

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

KEYWORDS

Clothing Size, Recommender System, Collaborative Filtering, Personalization, Fashion E-Commerce, Machine Learning

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Personalized Size Recommender System for Online Apparel Shopping: A Collaborative Filtering Approach

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Personalized Size Recommender System for Online Apparel Shopping: A Collaborative Filtering Approach

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.