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Open AccessArticle
Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach
by
Minseo Park
Minseo Park † and
Jangmin Oh
Jangmin Oh *,†
School of AI Convergence, Seoul 02844, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2024, 14(16), 7255; https://doi.org/10.3390/app14167255 (registering DOI)
Submission received: 20 July 2024
/
Revised: 15 August 2024
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Accepted: 15 August 2024
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Published: 17 August 2024
Abstract
This study proposes how to incorporate concurrent purchase data into e-commerce recommendation systems to improve their predictive accuracy. We identified that concurrent purchases account for about of total orders on Katcher’s, a Korean e-commerce platform. Despite the prevalence of concurrent purchases, existing algorithms often overlook this aspect. We introduce a novel transformer-based recommendation algorithm to process a user’s order history, including concurrent purchases. Each order is represented as a natural language sentence, capturing the order timestamp, product names and their attribute values, their corresponding categories, and whether multiple products were purchased together in a single order (i.e., a concurrent purchase). These sentences form a sequence, which serves as a training dataset for fine-tuning Bidirectional Encoder Representations from Transformers (BERT) with the Next Sentence Prediction objective. We validate our ideas by conducting experiments on Katcher’s platform, demonstrating the proposed method’s improved prediction performance compared to existing recommendation systems, with enhanced accuracy and F1 score. Notably, the normalized discounted cumulative gain (NDCG) showed a significant improvement with a large margin. Furthermore, we demonstrate the beneficial impact of integrating concurrent purchase information on prediction performance.
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MDPI and ACS Style
Park, M.; Oh, J.
Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach. Appl. Sci. 2024, 14, 7255.
https://doi.org/10.3390/app14167255
AMA Style
Park M, Oh J.
Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach. Applied Sciences. 2024; 14(16):7255.
https://doi.org/10.3390/app14167255
Chicago/Turabian Style
Park, Minseo, and Jangmin Oh.
2024. "Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach" Applied Sciences 14, no. 16: 7255.
https://doi.org/10.3390/app14167255
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