结合视觉分割与趋势感知，提升时尚推荐的风格匹配与流行度平衡。
Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
- 用语义分割聚焦服装区域，提取深层视觉特征
- ResNet-50在类别相似度达64.95%，流行度误差最低
- 适合个性化时尚推荐系统开发者参考
我们提出一种趋势感知且视觉基础的时尚推荐系统，融合深度视觉表征、服饰感知分割、语义类别相似性及用户行为模拟。通过语义分割掩蔽非服饰区域，再使用预训练CNN骨干网络（ResNet-50、DenseNet-121、VGG16）提取聚焦视觉嵌入。为模拟真实购物行为，生成受用户趋势偏好和商品流行度影响的合成购买历史。推荐结果基于加权评分函数，融合视觉相似性、语义一致性与流行度对齐。在DeepFashion数据集上的实验表明，系统实现一致的性别对齐与更高类别相关性，其中ResNet-50达到64.95%的类别相似度，且流行度均方误差最低。消融实验证明视觉与流行度线索具有互补作用。本方法为个性化时尚推荐提供可扩展框架，兼顾个人风格与新兴趋势。代码已开源。
原文摘要 · Abstract (English)
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
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