Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization
The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nua...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4591 |
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| author | Chunhui Zhang Xiaofen Ji Liling Cai |
| author_facet | Chunhui Zhang Xiaofen Ji Liling Cai |
| author_sort | Chunhui Zhang |
| collection | DOAJ |
| description | The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their ability to deliver truly personalized suggestions. To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. An attention mechanism optimizes multimodal fusion, enabling robust modeling of user preferences. Comparative experiments demonstrate that DeepFMP outperforms state-of-the-art baselines (LR, FM, Wide & Deep, GP-BPR, and DeepFM), achieving AUC improvements of 1.6–12.2% and NDCG@10 gains of up to 3.2%. Case analyses further reveal that DeepFMP effectively improves the recommendation accuracy, offering actionable insights for personalized marketing. This work advances multimodal recommendation systems by emphasizing price sensitivity as a pivotal factor, providing a scalable solution for enhancing user satisfaction and commercial efficacy in fashion e-commerce. |
| format | Article |
| id | doaj-art-d34d9700b86e43f590fc6d4f95a37e11 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d34d9700b86e43f590fc6d4f95a37e112025-08-20T02:28:16ZengMDPI AGApplied Sciences2076-34172025-04-01158459110.3390/app15084591Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization OptimizationChunhui Zhang0Xiaofen Ji1Liling Cai2School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaInternational Fashion Technology College, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaInternational Fashion Technology College, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaThe rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their ability to deliver truly personalized suggestions. To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. An attention mechanism optimizes multimodal fusion, enabling robust modeling of user preferences. Comparative experiments demonstrate that DeepFMP outperforms state-of-the-art baselines (LR, FM, Wide & Deep, GP-BPR, and DeepFM), achieving AUC improvements of 1.6–12.2% and NDCG@10 gains of up to 3.2%. Case analyses further reveal that DeepFMP effectively improves the recommendation accuracy, offering actionable insights for personalized marketing. This work advances multimodal recommendation systems by emphasizing price sensitivity as a pivotal factor, providing a scalable solution for enhancing user satisfaction and commercial efficacy in fashion e-commerce.https://www.mdpi.com/2076-3417/15/8/4591deep learningmultimodal fusionconsumer preferencegarment recommendationprice sensitivity |
| spellingShingle | Chunhui Zhang Xiaofen Ji Liling Cai Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization Applied Sciences deep learning multimodal fusion consumer preference garment recommendation price sensitivity |
| title | Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization |
| title_full | Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization |
| title_fullStr | Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization |
| title_full_unstemmed | Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization |
| title_short | Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization |
| title_sort | clothing recommendation with multimodal feature fusion price sensitivity and personalization optimization |
| topic | deep learning multimodal fusion consumer preference garment recommendation price sensitivity |
| url | https://www.mdpi.com/2076-3417/15/8/4591 |
| work_keys_str_mv | AT chunhuizhang clothingrecommendationwithmultimodalfeaturefusionpricesensitivityandpersonalizationoptimization AT xiaofenji clothingrecommendationwithmultimodalfeaturefusionpricesensitivityandpersonalizationoptimization AT lilingcai clothingrecommendationwithmultimodalfeaturefusionpricesensitivityandpersonalizationoptimization |