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Dataset

Hnm Search Data

Name: Hnm Search Data
Creator: Rajeev Gupta

by Rajeev Gupta hf-dataset--rajeev-gupta--hnm-search-data

Nexus Index

40.3 Top 100%

S / A / P / R / Q Breakdown Calibration Pending

Pillar scores are computed during the next indexing cycle.

Tech Context

Vital Performance

0 DL / 30D

0.0%

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Data Integrity 40.3 FNI Score

- Size

- Rows

Parquet Format

- Tokens

Dataset Information Summary
Entity Passport
Registry ID	hf-dataset--rajeev-gupta--hnm-search-data
Provider	huggingface

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Cite this dataset

Academic & Research Attribution

BibTeX

@misc{hf_dataset__rajeev_gupta__hnm_search_data,
  author = {Rajeev Gupta},
  title = {Hnm Search Data Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/rajeev-gupta/hnm-search-data}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Rajeev Gupta. (2026). Hnm Search Data [Dataset]. Free2AITools. https://huggingface.co/datasets/rajeev-gupta/hnm-search-data

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Nexus Index V2.0

Methodology Index Protocol

40.3

ESTIMATED IMPACT TIER

Semantic (S) 0

Authority (A) 0

Popularity (P) 0

Recency (R) 0

Quality (Q) 0

💬 Index Insight

FNI V2.0 for Hnm Search Data: Semantic (S:0), Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:0).

Free2AITools Nexus Index

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Downloads

166,093

👁️ Data Preview

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Schema structure is shown in the Field Logic panel when available.

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🧬 Field Logic

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Schema not yet indexed for this dataset.

Dataset Specification

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`

column	Description	Type
`query_id`	Unique ID for the query(Primary Key)	`object` (String)
`transaction_id`	Unique ID for the transaction(Foreign Key)	`object` (String)
`query_text`	Text of the query	`object` (String)

`qrels.csv`

column	Description	Type
`query_id`	ID for the query(Foreign Key)	`object` (String)
`positive_ids`	ID for the positive result(Foreign Key) which the user clicked/purchased	`object` (String)
`negative_ids`	Space separated list of IDs for the negative result(Foreign Key) which the user didn't click/purchase	`object` (String)

📌 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.

📊 Structured Schema (Zero-Fabrication)

Feature Key	Data Type
`article_id`	`int64`
`product_code`	`int64`
`prod_name`	`string`
`product_type_no`	`int64`
`product_type_name`	`string`
`product_group_name`	`string`
`graphical_appearance_no`	`int64`
`graphical_appearance_name`	`string`
`colour_group_code`	`int64`
`colour_group_name`	`string`
`perceived_colour_value_id`	`int64`
`perceived_colour_value_name`	`string`
`perceived_colour_master_id`	`int64`
`perceived_colour_master_name`	`string`
`department_no`	`int64`
`department_name`	`string`
`index_code`	`string`
`index_name`	`string`
`index_group_no`	`int64`
`index_group_name`	`string`
`section_no`	`int64`
`section_name`	`string`
`garment_group_no`	`int64`
`garment_group_name`	`string`
`detail_desc`	`string`

Estimated Rows: 105,542

Social Proof

HuggingFace Hub

166.1KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Dataset Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id: hf-dataset--rajeev-gupta--hnm-search-data
slug: rajeev-gupta--hnm-search-data
source: huggingface
author: Rajeev Gupta
license
tags: task_categories:text-ranking, task_categories:text-retrieval, task_categories:text-classification, language:en, size_categories:10m

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 166,093
stars: 0
forks: 0

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