Hybrid fashion recommendation system — combines collaborative filtering with LLM-based reranking to deliver personalized outfit suggestions that understand style context.
| Model | Precision@10 | Recall@10 | NDCG@10 |
|---|---|---|---|
| Baseline CF | 0.24 | 0.31 | 0.28 |
| CF + LLM Rerank | 0.31 | 0.38 | 0.35 |
| Improvement | +29% | +23% | +25% |
git clone https://github.com/Jkanishkha0305/fashion-recsys.git
cd fashion-recsys
pip install -r requirements.txt
# Train collaborative filtering model
python train.py --model cf --epochs 20
# Launch recommendation UI
streamlit run app.py- Collaborative Filtering — matrix factorization baseline
- LLM Reranking — GPT-4 reranks CF candidates using style context
- PyTorch — model training
- Streamlit — interactive recommendation UI