Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning
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. H...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2463723 |
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| _version_ | 1850140123024326656 |
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| author | Tsung-Yin Ou Chun-Hung Chen Wen-Lung Tsai |
| author_facet | Tsung-Yin Ou Chun-Hung Chen Wen-Lung Tsai |
| author_sort | Tsung-Yin Ou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6d9895eb5a834c0fae59d4dff4a404ba |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-6d9895eb5a834c0fae59d4dff4a404ba2025-08-20T02:29:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2463723Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep LearningTsung-Yin Ou0Chun-Hung Chen1Wen-Lung Tsai2Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanCollege of Management, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanDepartment of Information Management, National Taipei University of Business, Taipei, TaiwanThe 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.https://www.tandfonline.com/doi/10.1080/08839514.2025.2463723 |
| spellingShingle | Tsung-Yin Ou Chun-Hung Chen Wen-Lung Tsai Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning Applied Artificial Intelligence |
| title | Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning |
| title_full | Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning |
| title_fullStr | Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning |
| title_full_unstemmed | Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning |
| title_short | Establishing a Dynamic Recommendation System for E-commerce by Integrating Online Reviews, Product Feature Expansion, and Deep Learning |
| title_sort | establishing a dynamic recommendation system for e commerce by integrating online reviews product feature expansion and deep learning |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2463723 |
| work_keys_str_mv | AT tsungyinou establishingadynamicrecommendationsystemforecommercebyintegratingonlinereviewsproductfeatureexpansionanddeeplearning AT chunhungchen establishingadynamicrecommendationsystemforecommercebyintegratingonlinereviewsproductfeatureexpansionanddeeplearning AT wenlungtsai establishingadynamicrecommendationsystemforecommercebyintegratingonlinereviewsproductfeatureexpansionanddeeplearning |