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hm_ecommerce_products

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fashion
recommendation
embeddings
retail
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metadata
license: cc-by-4.0
task_categories:
  - image-classification
  - text-classification
  - image-to-text
  - image-feature-extraction
tags:
  - fashion
  - recommendation
  - embeddings
  - retail
  - e-commerce
  - multimodal
pretty_name: H&M Fashion - Enhanced with Embeddings
size_categories:
  - 100K<n<1M

H&M Personalized Fashion Recommendations - Enhanced Dataset

Dataset Description

This dataset is a processed and enhanced version of the H&M Personalized Fashion Recommendations Kaggle competition dataset. The original dataset has been cleaned and augmented with pre-computed embeddings and accessible image URLs to facilitate fashion recommendation research and multimodal retrieval applications.

Dataset Summary

The H&M dataset contains rich product metadata, customer information, and transaction history from H&M's e-commerce platform. This enhanced version includes:

  • ~106,000 fashion products with detailed metadata
  • ~1.37 million customers with demographic information
  • Historical transaction data for recommendation modeling
  • Product images accessible via S3 URLs
  • Pre-computed dense embeddings (BGE-small-en-1.5)
  • Pre-computed sparse embeddings (SPLADEv1)

Languages

  • English (product descriptions and metadata)

Enhancements Over Original Dataset

This processed version includes the following improvements:

  1. Data Cleaning: Removed all null values for improved data quality
  2. Image Accessibility: All product images uploaded to S3 with direct URL access via image_url field
  3. Dense Embeddings: Pre-computed BGE-small-en-1.5 embeddings for efficient semantic search
  4. Sparse Embeddings: Pre-computed SPLADEv1 embeddings for keyword-based retrieval

Dataset Structure

Data Instances

Each article (product) record contains:

{
  "article_id": "108775015",
  "product_code": "108775",
  "prod_name": "Strap top",
  "product_type_no": 253,
  "product_type_name": "Vest top",
  "product_group_name": "Garment Upper body",
  "graphical_appearance_no": 1010016,
  "graphical_appearance_name": "Solid",
  "colour_group_code": 9,
  "colour_group_name": "Black",
  "perceived_colour_value_id": 4,
  "perceived_colour_value_name": "Dark",
  "perceived_colour_master_id": 5,
  "perceived_colour_master_name": "Black",
  "department_no": 1676,
  "department_name": "Jersey Basic",
  "index_code": "A",
  "index_name": "Ladieswear",
  "index_group_no": 1,
  "index_group_name": "Ladieswear",
  "section_no": 16,
  "section_name": "Womens Everyday Basics",
  "garment_group_no": 1002,
  "garment_group_name": "Jersey Basic",
  "detail_desc": "Fitted strap top in soft jersey with narrow shoulder straps.",
  "image_url": "https://your-bucket.s3.amazonaws.com/108775015.jpg",
  "bge_embedding": [0.123, -0.456, ...],  // 384-dimensional vector
  "splade_embedding": {"token_id": weight, ...}  // Sparse vector representation
}

Data Fields

Article Metadata

  • article_id (string): Unique product identifier
  • product_code (string): Product family code
  • prod_name (string): Product name
  • product_type_no (int): Product type numeric code
  • product_type_name (string): Product type description
  • product_group_name (string): High-level product category
  • graphical_appearance_no (int): Pattern code
  • graphical_appearance_name (string): Pattern description (e.g., "Solid", "Stripe")
  • colour_group_code (int): Color category code
  • colour_group_name (string): Color category name
  • perceived_colour_value_id (int): Color brightness/darkness code
  • perceived_colour_value_name (string): Color brightness description
  • perceived_colour_master_id (int): Master color category code
  • perceived_colour_master_name (string): Master color name
  • department_no (int): Department code
  • department_name (string): Department name
  • index_code (string): Index category code
  • index_name (string): Index category (e.g., "Ladieswear", "Menswear")
  • index_group_no (int): Index group numeric code
  • index_group_name (string): Index group name
  • section_no (int): Section code
  • section_name (string): Section description
  • garment_group_no (int): Garment group code
  • garment_group_name (string): Garment group name
  • detail_desc (string): Detailed product description

Enhanced Fields

  • image_url (string): Direct S3 URL to product image
  • bge_embedding (list[float]): 384-dimensional dense embedding from BGE-small-en-1.5
  • splade_embedding (dict): Sparse embedding from SPLADEv1 with token weights

Data Splits

The dataset maintains the original temporal structure:

  • Training period: 2018-09-20 to 2020-09-22
  • Prediction target: Week of 2020-09-23 to 2020-09-29

Dataset Creation

Source Data

The original dataset was created by H&M Group for a Kaggle competition aimed at developing personalized fashion recommendation systems. The data represents real customer transactions and product catalog information.

Processing Pipeline

  1. Data Loading: Original CSV files from Kaggle competition
  2. Null Removal: Filtered out records with missing critical fields
  3. Image Upload: Product images uploaded to S3 for reliable access
  4. Embedding Generation:
    • BGE-small-en-1.5: Dense embeddings generated from product descriptions and metadata
    • SPLADEv1: Sparse embeddings for efficient lexical matching
  5. Quality Validation: Verified embedding dimensions and data integrity

Embedding Models

BGE-small-en-1.5

  • Model: BAAI/bge-small-en-v1.5
  • Dimension: 384
  • Use Case: Dense semantic retrieval, similarity search
  • Input: Product descriptions and relevant metadata fields

SPLADEv1

  • Model: SPLADE (Sparse Lexical and Expansion Model)
  • Type: Learned sparse representation
  • Use Case: Keyword-based retrieval, interpretable search

Uses

Direct Use

This dataset is ideal for:

  • Fashion recommendation systems: Product-to-product and user-to-product recommendations
  • Multimodal retrieval: Combining text and image features for search
  • Embedding evaluation: Benchmarking dense and sparse retrieval methods
  • Fashion trend analysis: Understanding product categorization and customer preferences
  • Vector database applications: Testing and demonstrating retrieval systems (e.g., with Qdrant, Pinecone, Weaviate)

Use Cases 

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/hm-fashion-enhanced")

# Example 1: Semantic search using BGE embeddings
query_embedding = model.encode("casual summer dress")
# Use embeddings for similarity search in your vector database

# Example 2: Hybrid search combining dense and sparse
# Combine BGE dense embeddings with SPLADE sparse for best results

# Example 3: Fashion recommendation
# Use transaction history + embeddings for personalized recommendations

Considerations for Using the Data

Social Impact

Fashion recommendation systems can influence purchasing decisions and consumer behavior. Consider:

  • Diversity: Ensure recommendations don't reinforce narrow beauty standards
  • Inclusivity: Product recommendations should serve diverse customer demographics
  • Sustainability: Consider environmental impact of fast fashion recommendations

Limitations

  • Temporal Scope: Data covers 2018-2020; fashion trends evolve rapidly
  • Geographic Bias: Primarily represents European H&M markets
  • Demographic Representation: Customer demographics may not represent global population
  • Cold Start: Limited data for new products and customers
  • Null Removal: Some products excluded due to missing data

Privacy

  • Customer data has been anonymized
  • No personally identifiable information (PII) included
  • Transaction history anonymized with customer IDs

Additional Information

Dataset Curators

Original dataset: H&M Group via Kaggle Enhanced version: [Your name/organization]

Licensing Information

The dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, following the original Kaggle competition terms.

Citation

If you use this dataset, please cite both the original competition and this enhanced version:

@misc{hm-fashion-kaggle,
  title={H\&M Personalized Fashion Recommendations},
  author={H\&M Group},
  year={2022},
  howpublished={Kaggle Competition},
  url={https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations}
}

@misc{hm-fashion-enhanced,
  title={H\&M Personalized Fashion Recommendations - Enhanced with Embeddings},
  author={[Your Name]},
  year={2024},
  howpublished={HuggingFace Datasets},
  url={[your dataset URL]}
}

Contributions

Contributions and feedback are welcome! Please open an issue or pull request if you find any problems or have suggestions for improvements.

Acknowledgments

  • H&M Group for providing the original dataset
  • Kaggle for hosting the competition
  • BAAI for the BGE embedding model
  • NAVER for the SPLADE sparse embedding model

Contact

For questions or issues, please contact [your contact information] or open an issue on the dataset repository.

Example Notebooks

Coming soon:

  • Fashion retrieval with Qdrant vector database
  • Hybrid dense-sparse search implementation
  • Building a personalized recommendation engine
  • Multi-modal fashion search (text + image)