Plus-Size Clothing Recommendation System Based on Sales Transaction Data Using FP-Growth Algorithm
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Abstract
The recommendation systems for the fashion industry have been widely studied. However, a study on the recommendation systems for the plus-size fashion market is limited. This study investigates the plus-size clothing sales recommendation system on an e commerce platform in Thailand using the FP-Growth algorithm. A total of 26,993 transaction records were collected during January 2021 to December 2022. The significance of the discovered rules is also examined to gain implicit knowledge about customer preferences in the plus-size clothing domain. The findings show the lists of association rules of the items that customers frequently purchase together. The model has a precision value of 7.88%, a recall of 4.23%, and an F1-score of 5.50%. The relatively low performance of the FP-Growth algorithm indicated the challenges of developing a recommendation system in this domain due to the limitation of the dataset and the diverse purchasing behavior of the plus-size customers. This study fills a research gap and provides a foundation for future research in recommendation systems for plus size clothing.
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