README.md

metadata

dataset_info:
  - config_name: interactions
    features:
      - name: user_id
        dtype: string
      - name: item_id
        dtype: string
      - name: interaction_type
        dtype: string
      - name: date
        dtype: string
    splits:
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        num_examples: 11589
      - name: valid
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        num_examples: 11035
      - name: test
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        num_examples: 11055
    download_size: 452442
    dataset_size: 2260172
  - config_name: items
    features:
      - name: item_id
        dtype: string
      - name: master_category
        dtype: string
      - name: product_name
        dtype: string
      - name: price
        dtype: float64
      - name: image
        dtype: image
      - name: release_date
        dtype: string
      - name: dominant_color
        dtype: string
    splits:
      - name: train
        num_bytes: 176665107.361
        num_examples: 6589
      - name: valid
        num_bytes: 162871483.145
        num_examples: 6035
      - name: test
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        num_examples: 6055
    download_size: 500916281
    dataset_size: 504739286.50100005
  - config_name: kits
    features:
      - name: kit_id
        dtype: string
      - name: kit_name
        dtype: string
      - name: description
        dtype: string
      - name: user_id
        dtype: string
      - name: image
        dtype: image
      - name: views
        dtype: int64
      - name: likes
        dtype: int64
      - name: date
        dtype: string
    splits:
      - name: train
        num_bytes: 88684872
        num_examples: 1000
      - name: valid
        num_bytes: 94284866
        num_examples: 1000
      - name: test
        num_bytes: 94297278
        num_examples: 1000
    download_size: 276365750
    dataset_size: 277267016
  - config_name: user_profiles
    features:
      - name: user_id
        dtype: string
      - name: preferred_colors
        list: string
    splits:
      - name: train
        num_bytes: 27927
        num_examples: 993
      - name: valid
        num_bytes: 28042
        num_examples: 999
      - name: test
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        num_examples: 993
    download_size: 32224
    dataset_size: 83935
  - config_name: users
    features:
      - name: user_id
        dtype: string
      - name: user_name
        dtype: string
    splits:
      - name: train
        num_bytes: 31000
        num_examples: 1000
      - name: valid
        num_bytes: 31000
        num_examples: 1000
      - name: test
        num_bytes: 31000
        num_examples: 1000
    download_size: 64058
    dataset_size: 93000
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 :

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.