Version 1
: Received: 27 September 2024 / Approved: 30 September 2024 / Online: 30 September 2024 (14:37:58 CEST)
How to cite:
Li, S. Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce. Preprints2024, 2024092417. https://doi.org/10.20944/preprints202409.2417.v1
Li, S. Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce. Preprints 2024, 2024092417. https://doi.org/10.20944/preprints202409.2417.v1
Li, S. Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce. Preprints2024, 2024092417. https://doi.org/10.20944/preprints202409.2417.v1
APA Style
Li, S. (2024). Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce. Preprints. https://doi.org/10.20944/preprints202409.2417.v1
Chicago/Turabian Style
Li, S. 2024 "Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce" Preprints. https://doi.org/10.20944/preprints202409.2417.v1
Abstract
Recommending products effectively based on historical transaction data and metadata is crucial for improving user satisfaction and increasing sales. This study presents a recommendation system evaluated using Mean Average Precision (MAP@12), leveraging a diverse dataset that includes apparel types, customer demographics, product descriptions, and images. Our approach integrates Mult-Recall methods and LightGBM Ranker, utilizing various recall strategies and advanced feature engineering techniques to enhance recommendation accuracy. By combining these advanced machine learning models and ensemble techniques, our proposed solution outperforms existing methods, providing a robust and efficient recommendation system tailored to diverse customer needs and product characteristics. This integrated approach not only addresses the challenges of data sparsity and cold start but also adapts to the dynamic nature of user preferences and market trends. The results demonstrate significant improvements in both recommendation precision and user satisfaction, highlighting the potential of our approach for real-world e-commerce applications.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
