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mudasir13cs
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ecommerce-query-rewriting

Tasks:
Text Generation
Question Answering
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English
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e-commerce
query-rewriting
context-aware
conversational-search
conversational-ai
nlp
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metadata 
license: cc-by-sa-4.0
language:
  - en
task_categories:
  - text-generation
  - question-answering
tags:
  - e-commerce
  - query-rewriting
  - context-aware
  - conversational-search
  - conversational-ai
  - nlp
  - llm
  - fine-tuning
  - intent-classification
  - dialogue-systems
  - retail
  - search
pretty_name: E-Commerce Query Rewriting Dataset
size_categories:
  - 1K<n<10K

#e-commerce-query-rewriting-dataset E-Commerce Query Rewriting Dataset

Hub: mudasir13cs/ecommerce-query-rewriting

A dataset of 10,000 examples pairing ambiguous, context-dependent user queries with their fully resolved, context-aware rewrites for e-commerce product search. Built for fine-tuning LLMs to resolve pronouns, ellipsis, ordinals, and other conversational shortcuts using prior search context — the kind of resolution real shopping assistants need to handle turns like "show me that one" or "the cheaper one".

#dataset-summary Dataset summary

Conversational commerce agents constantly receive queries that only make sense given prior turns — a pronoun ("it"), an ordinal ("the second one"), or a fragment ("with better camera"). This dataset provides supervised pairs of (ambiguous query, conversation context) → (unambiguous rewritten query) so models can learn to perform this resolution reliably before handing the query off to search or a product-detail lookup.

Key features

  • Real-world-style e-commerce data spanning Flipkart, Amazon, and MyOnlineShop product catalogs
  • Rich conversational context: previous search term, UI state, product list, and last executed command
  • 7 distinct rewriting patterns covering the most common ambiguity types in shopping dialogue
  • Variable product-list sizes (1–10 items) for realistic search-result scenarios
  • Ready-to-use instruction-tuning format for both query rewriting and intent classification

#dataset-structure Dataset structure

Each example contains:

Field Type Description
original_query string Ambiguous user query requiring context resolution
context.last_search_query string Previous search category or term
context.current_state string UI state — SEARCH_RESULTS, PRODUCT_DETAIL, or INITIAL
context.product_titles list[string] Product names from the last search (1–10 items)
context.last_command string Previously executed system command
context.product_count int Number of products in context (1–10)
rewritten_query string Clear, unambiguous query with references resolved
intent string User intent — search, show_detail, go_back, close
command string System command — show_list, show_item, go_back, close

Example

{
  "original_query": "show me that one",
  "context": {
    "last_search_query": "Smartphones",
    "current_state": "SEARCH_RESULTS",
    "product_titles": [
      "iPhone 15 Pro",
      "Samsung Galaxy S24",
      "OnePlus 12",
      "Google Pixel 8",
      "Xiaomi 14",
      "Nothing Phone 2"
    ],
    "last_command": "show_list",
    "product_count": 6
  },
  "rewritten_query": "show me details of iPhone 15 Pro from Smartphones search",
  "intent": "show_detail",
  "command": "show_item"
}

#dataset-statistics Dataset statistics

Overall

  • Total examples: 10,000
  • Product sources: Flipkart, Amazon, MyOnlineShop
  • Format: JSON, instruction–input–output ready

Intent distribution

Intent Count %
show_detail 6,000 60.0%
search 3,500 35.0%
navigation 500 5.0%

Command distribution

Command Count %
show_item 6,000 60.0%
show_list 3,500 35.0%
go_back 500 5.0%

State distribution

State Count %
SEARCH_RESULTS 8,024 80.2%
PRODUCT_DETAIL 1,976 19.8%

#query-rewriting-patterns Query rewriting patterns

# Pattern Share Example
1 Pronoun resolution ~30% "show me that one" → "show me details of iPhone 15 Pro from Smartphones search"
2 Ellipsis ~20% "find cheaper" → "find Smartphones cheaper than 50000"
3 Ordinals ~15% "show me first" → "show me details of iPhone 15 Pro from Smartphones search"
4 Product references ~15% "tell me about iPhone" → "show me details of iPhone 15 Pro from Smartphones search"
5 Price / category ~10% "show me under 50000" → "show me Smartphones under 50000"
6 Navigation ~5% "go back" → "return to Smartphones search results"
7 Refinements ~5% "higher rating" → "find Smartphones higher rating"

#loading-the-dataset Loading the dataset

Using 🤗 Datasets (recommended)

from datasets import load_dataset

dataset = load_dataset("mudasir13cs/ecommerce-query-rewriting")

train_data = dataset["train"]
print(train_data[0])

Direct JSON loading

import json

with open("dataset_complete.json", "r") as f:
    data = json.load(f)

#training-formats Training formats

Query rewriter

{
  "instruction": "Rewrite the ambiguous query using the provided context to make it clear and searchable.",
  "input": "Context:\nPrevious search: Smartphones\nState: SEARCH_RESULTS\nProducts (6): iPhone 15 Pro, Samsung Galaxy S24, ...\n\nQuery: show me that one",
  "output": "show me details of iPhone 15 Pro from Smartphones search"
}

Intent classifier

{
  "instruction": "Classify the user's intent based on the query and context.",
  "input": "Context:\nPrevious search: Smartphones\nState: SEARCH_RESULTS\nProducts (6): iPhone 15 Pro, Samsung Galaxy S24, ...\n\nQuery: show me that one",
  "output": "show_detail"
}

#use-cases Use cases

  • Fine-tuning query rewriting models — train LLMs to resolve ambiguous queries with (context, query) → rewritten_query pairs
  • Intent classification — train classifiers on the intent field for dialogue-state routing
  • Conversational search systems — build production agents that maintain and resolve context across turns
  • Research & evaluation — benchmark rewriting approaches, study ambiguity resolution, and analyze conversational search patterns
def rewrite_query(query, context):
    # Use a fine-tuned model to resolve the query against context
    return model.predict(query, context)

#quality-assurance Quality assurance

Every example is validated for:

  • Presence of both original_query and rewritten_query
  • Complete, well-formed context object
  • Rewritten query is strictly longer and more specific than the original
  • Valid intent and command values
  • product_count within the valid range (1–10)

#citation Citation

If you use this dataset in your research, please cite:

@dataset{ecommerce_query_rewriting_2026,
  title={E-Commerce Query Rewriting Dataset for Context-Aware Conversational Search},
  author={Syed Mudasir},
  year={2026},
  url={https://huggingface.co/datasets/mudasir13cs/ecommerce-query-rewriting},
  note={Dataset for fine-tuning context-aware query rewriting models in e-commerce}
}

#license License

Released under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

  • ✅ Commercial use allowed
  • ✅ Modification allowed
  • ✅ Distribution allowed
  • ✅ Private use allowed
  • ⚠️ Attribution required
  • ⚠️ ShareAlike — derivatives must use the same license

#acknowledgments Acknowledgments

  • Product data sources: Flipkart, Amazon, MyOnlineShop
  • The open-source ML community for tooling and frameworks
  • Hugging Face for dataset hosting infrastructure

#author--contact Author / contact

Mudasir — multimodal AI, VLM fine-tuning, retrieval/RAG research, and applied NLP engineering; MS AI Convergence, Soongsil University, Seoul. More projects and publications: mudasir13cs.github.io

Version: 1.0 · Last updated: July 2026