---
dataset_info:
- config_name: interactions
  features:
  - name: user_id
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  - name: item_id
    dtype: string
  - name: interaction_type
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  - name: date
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    dtype: string
  - name: master_category
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  - name: product_name
    dtype: string
  - name: price
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  - name: image
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  - name: release_date
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- config_name: kits
  features:
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    dtype: string
  - name: kit_name
    dtype: string
  - name: description
    dtype: string
  - name: user_id
    dtype: string
  - name: image
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  - name: likes
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  - name: date
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- config_name: user_profiles
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    dtype: string
  - name: preferred_colors
    list: string
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  features:
  - name: user_id
    dtype: string
  - name: user_name
    dtype: string
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  - name: valid
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    num_examples: 1000
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configs:
- config_name: interactions
  data_files:
  - split: train
    path: interactions/train-*
  - split: valid
    path: interactions/valid-*
  - split: test
    path: interactions/test-*
- config_name: items
  data_files:
  - split: train
    path: items/train-*
  - split: valid
    path: items/valid-*
  - split: test
    path: items/test-*
- config_name: kits
  data_files:
  - split: train
    path: kits/train-*
  - split: valid
    path: kits/valid-*
  - split: test
    path: kits/test-*
- config_name: user_profiles
  data_files:
  - split: train
    path: user_profiles/train-*
  - split: valid
    path: user_profiles/valid-*
  - split: test
    path: user_profiles/test-*
- config_name: users
  data_files:
  - split: train
    path: users/train-*
  - split: valid
    path: users/valid-*
  - split: test
    path: users/test-*
---
=====================README====================


# Polyvore‑1000 Dataset

Bienvenue ! Je suis **Waly NGOM**, docteur en mathématiques et passionné par l’intelligence artificielle. Ce dépôt contient **Polyvore‑1000**, un dataset conçu pour la recommandation personnalisée dans le domaine de la mode.

Polyvore‑1000 s’appuie sur les **splits Polyvore‑U** conçus par Han et al. (2017) et bénéficie du travail complémentaire de Lu et al. (CVPR 2019), qui ont apporté une approche innovante basée sur des codes binaires pour la recommandation efficace d’outfits.

##  Structure des données

- **Splits disponibles** : `train`, `valid`, `test` (mêmes proportions que Polyvore‑U : 17 316 / 1 497 / 3 076 outfits).
- **Configurations** :
  - `items` : données détaillées des items
  - `kits` : informations sur chaque outfit
  - `users` : identifiants synthétiques d’utilisateurs
  - `interactions` : interactions entre utilisateurs et items (composition d’outfits, vues, likes)

###  Images

Les images sont organisées dans `images/<kit_id>/` :
- `0.jpg` → image de l’outfit (kit)
- `1.jpg`, `2.jpg`, … → images correspondant aux items du kit, dans l’ordre des données JSON



### Authentification Hugging Face

Dans un notebook ou script Python :

```python
from huggingface_hub import login
import os

login(token=os.getenv("HF_TOKEN"))


##  Utilisation

Pour charger ces datasets:

from datasets import load_dataset

items_ds = load_dataset("codewaly/polyvore1000", "items", split="train")
kits_ds = load_dataset("codewaly/polyvore1000", "kits", , split="train")
users_ds = load_dataset("codewaly/polyvore1000", "users", split="train")
interactions_ds = load_dataset("codewaly/polyvore1000", "interactions", split="train")


Références

1. Han, X., et al. (2017). Learning Fashion Compatibility with Bidirectional LSTMs. ACM Multimedia.

2. Lu, Z., et al. (2019). Learning Binary Code for Personalized Fashion Recommendation. CVPR.