Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity

• URL: http://arxiv.org/abs/2506.07773v1
• Date: Mon, 09 Jun 2025 13:48:16 GMT
• Title: Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
• Authors: Mohamed Djilani, Nassim Ali Ousalah, Nidhal Eddine Chenni,
• Abstract summary: 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.<n>Our pipeline extracts focused visual embeddings by masking non-garment regions via semantic segmentation.<n>To simulate realistic shopping behavior, we generate synthetic purchase histories influenced by user-specific trendiness and item popularity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions.<n>For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• Design-time Fashion Popularity Forecasting in VR Environments [9.621518697689128]
  We develop a computer vision pipeline fine tuned on fashion imagery to extract relevant visual features. We propose MuQAR, a Multimodal Quasi-AutoRegressive neural network that forecasts the popularity of new garments. Both the proposed HLS and MuQAR prove capable of surpassing the current state-of-the-art in key benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-14T12:30:03Z)
• PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions [0.5801044612920815]
  We propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement.
  arXiv  Detail & Related papers  (2022-09-16T12:07:43Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition [80.74495836502919]
  In this work, we focus on joint human fashion segmentation and attribute recognition. We introduce the object query for segmentation and the attribute query for attribute prediction. For attribute stream, we design a novel Multi-Layer Rendering module to explore more fine-grained features.
  arXiv  Detail & Related papers  (2022-04-10T11:11:10Z)
• Contextual Similarity Aggregation with Self-attention for Visual Re-ranking [96.55393026011811]
  We propose a visual re-ranking method by contextual similarity aggregation with self-attention. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method.
  arXiv  Detail & Related papers  (2021-10-26T06:20:31Z)
• Attribute-aware Explainable Complementary Clothing Recommendation [37.30129304097086]
  This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec) AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item's visual feature. The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes.
  arXiv  Detail & Related papers  (2021-07-04T14:56:07Z)
• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [66.04457457299218]
  We propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items. The learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
  arXiv  Detail & Related papers  (2020-05-06T14:17:05Z)
• Learning Diverse Fashion Collocation by Neural Graph Filtering [78.9188246136867]
  We propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset.
  arXiv  Detail & Related papers  (2020-03-11T16:17:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.