Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning - Tsung-Yin Ou; Chun-Hung Chen; Wen-Lung Tsai (Applied Artificial Intelligence)
The increasing fragmentation of the consumer journey complicates understanding consumer behavior and tracking digital footprints. Product feature tags should dynamically adapt to consumer preferences, marketing campaigns, and trending internet topics, leveraging crawler technology to address this. However, many e-commerce platforms still rely on manual tagging or static product attribute classification, with limited adoption of machine learning approaches. This study proposes a dynamic tagging and recommendation system using deep learning for product image recognition and similarity comparison. By integrating crawler technology, internet trends can serve as dynamic product tags. These tags, combined with consumer behavior data, enable the creation of a recommendation system capable of automatically generating relevant tags. Sales data from 3,132 cartoon products on a Taiwanese e-commerce platform were analyzed. A convolutional neural network was employed to establish a tagging and image recognition model, and it was trialed over 24 weeks. Results showed significant improvements in consumer engagement: average clicks per product increased by 36.1%, views by 22.9%, products added to carts by 32.3%, orders by 28.3%, and payment transactions by 30.4%. Aligning recommendation systems with consumer expectations enhances their ability to identify preferences and drive purchasing behavior.