Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
This work addresses fashion recommendation for users by balancing individual style with trends, but it is incremental as it combines existing methods like pretrained CNNs and segmentation.
The paper tackles the problem of personalized fashion recommendation by integrating visual segmentation, semantic similarity, and user behavior simulation, resulting in improved category relevance with ResNet-50 achieving 64.95% category similarity and low popularity error on the DeepFashion dataset.
We introduce a trend-aware and visually-grounded fashion recommendation system that integrates deep visual representations, garment-aware segmentation, semantic category similarity and user behavior simulation. Our pipeline extracts focused visual embeddings by masking non-garment regions via semantic segmentation followed by feature extraction using pretrained CNN backbones (ResNet-50, DenseNet-121, VGG16). To simulate realistic shopping behavior, we generate synthetic purchase histories influenced by user-specific trendiness and item popularity. Recommendations are computed using a weighted scoring function that fuses visual similarity, semantic coherence and popularity alignment. Experiments on the DeepFashion dataset demonstrate consistent gender alignment and improved category relevance, with ResNet-50 achieving 64.95% category similarity and lowest popularity MAE. An ablation study confirms the complementary roles of visual and popularity cues. Our method provides a scalable framework for personalized fashion recommendations that balances individual style with emerging trends. Our implementation is available at https://github.com/meddjilani/FashionRecommender