configs:

• config_name: articles
  data_files:
  • split: train
    path: data/raw/articles.csv
• config_name: customers
  data_files:
  • split: train
    path: data/raw/customers.csv
• config_name: transactions
  data_files:
  • split: train
    path: data/raw/transactions_train.csv
    dataset_info:
• config_name: articles
  features:
  • name: article_id
    dtype: int64
  • name: product_code
    dtype: int64
  • name: prod_name
    dtype: string
  • name: product_type_no
    dtype: int64
  • name: product_type_name
    dtype: string
  • name: product_group_name
    dtype: string
  • name: graphical_appearance_no
    dtype: int64
  • name: graphical_appearance_name
    dtype: string
  • name: colour_group_code
    dtype: int64
  • name: colour_group_name
    dtype: string
  • name: perceived_colour_value_id
    dtype: int64
  • name: perceived_colour_value_name
    dtype: string
  • name: perceived_colour_master_id
    dtype: int64
  • name: perceived_colour_master_name
    dtype: string
  • name: department_no
    dtype: int64
  • name: department_name
    dtype: string
  • name: index_code
    dtype: string
  • name: index_name
    dtype: string
  • name: index_group_no
    dtype: int64
  • name: index_group_name
    dtype: string
  • name: section_no
    dtype: int64
  • name: section_name
    dtype: string
  • name: garment_group_no
    dtype: int64
  • name: garment_group_name
    dtype: string
  • name: detail_desc
    dtype: string
• config_name: customers
  features:
  • name: customer_id
    dtype: string
  • name: FN
    dtype: float64
  • name: Active
    dtype: float64
  • name: club_member_status
    dtype: string
  • name: fashion_news_frequency
    dtype: string
  • name: age
    dtype: float64
  • name: postal_code
    dtype: string
• config_name: transactions
  features:
  • name: t_dat
    dtype: string
  • name: customer_id
    dtype: string
  • name: article_id
    dtype: int64
  • name: price
    dtype: float64
  • name: sales_channel_id
    dtype: int64
    task_categories:
• text-ranking
• text-retrieval
• text-classification
  language:
• en
  pretty_name: 'H&M Search Queries and Personalized Results '
  size_categories:
• 10M<n<100M
  tags:
• fashion
• e-commerce
• customer-behavior
• tabular
• recommendation-systems
• search
• ranking

HnM Search Dataset Created from Recommendations Dataset

This synthetic data-set is created using the recommendations dataset:

• https://huggingface.co/datasets/einrafh/hnm-fashion-recommendations-data (Use of this dataset is subject to the terms and conditions set forth on the original distribution page. This dataset is intended for non-commercial and research use.)
• https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data (DATA ACCESS AND USE: Non-Commercial Purposes & Academic Research.)

as base. The base dataset is a recommendations data set where transactions data has the articles purchased by the users. This dataset gives the search queries, which may have been issued by the user before buying the article, along with the candidate results.
The license for our additions is https://cdla.dev/permissive-2-0/

Search Queries Dataset

• queries.csv: 253685 List of queries for transactions.
• qrels.csv: 253685 List of positive and negative article-ids which were retrieved for each query.

Base Dataset

• articles.csv: 105542 List of unique products/articles with their properties/features.
• customers.csv: 1371980 List of unique customers/users with their properties/features.
• transactions_train.csv: 31788324 List of historical transactions/purchases of different articles by customers.

📂 Dataset Structure & Components

All search queries data is located in the folder 'data/search/' directory.

• data/search/queries.csv
  Queries generated from individual transactions (transactions_train.csv).
  (253685 rows, 3 columns: query_id, transaction_id, and query_text)
• data/search/qrels.csv
  Query results candidates-- positives (from the transaction) and close negatives article_ids (from articles.csv) .
  (253685 rows, 3 columns: query_id, positive_ids, negatives_ids (space separated))

All raw (recommendations) data is located in the data/raw/ directory.

• data/raw/transactions_train.csv
  A historical record of all purchase transactions. This file serves as a central table connecting customers with the articles they purchased.
  (31,788,324 rows, 5 columns)

• data/raw/customers.csv
  This dimension table contains attributes for each unique customer.
  (1,371,980 rows, 7 columns)

• data/raw/articles.csv
  This dimension table contains highly detailed attributes for each unique product (article).
  (105,542 rows, 25 columns)

• data/raw/images/
  This directory contains product images, organized into subdirectories based on the first 3 digits of the article_id.

🔗 Relationships Between Search Data

These files can be combined (joined) to create a comprehensive dataset for analysis:

query_id can be used to join the files queries.csv and qrels.csv to get the textual queries and the corresponding resultant articles.
Similarly, transaction_id (from queries.csv) can be used to get the details of corresponding transactions using transactions_train.csv.
positive_ids and negative_ids (from qrels.csv) can be used to join with articles.csv to get the details of the result articles (both positive-- which the user purchased-- and negatives)

📊Data Schema

Data schema for transactions_train.csv, 'customers.csv', and 'articles.csv' can be obtained from https://huggingface.co/datasets/einrafh/hnm-fashion-recommendations-data.
Here is the schema for the search data.

queries.csv

qrels.csv

📌 Source

The base dataset is provided to the public by H&M Group through the Kaggle platform for analysis and research purposes. We have added search queries over the base dataset.

• Platform: Kaggle, H&M Personalized Fashion Recommendations

⚠️ License

The use of this dataset is subject to the terms and conditions stated on its original distribution page. This dataset is intended for non-commercial and research purposes.