Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce

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.