README.md

---
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) E-Commerce Query Rewriting Dataset

**Hub:** [`mudasir13cs/ecommerce-query-rewriting`](https://huggingface.co/datasets/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)
- [Dataset structure](#dataset-structure)
- [Dataset statistics](#dataset-statistics)
- [Query rewriting patterns](#query-rewriting-patterns)
- [Loading the dataset](#loading-the-dataset)
- [Training formats](#training-formats)
- [Use cases](#use-cases)
- [Quality assurance](#quality-assurance)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Author / contact](#author--contact)

## [#dataset-summary](#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) 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**

```json
{
  "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) 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) 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) Loading the dataset

**Using 🤗 Datasets (recommended)**

```python
from datasets import load_dataset

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

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

**Direct JSON loading**

```python
import json

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

## [#training-formats](#training-formats) Training formats

**Query rewriter**

```json
{
  "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**

```json
{
  "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) 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

```python
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) 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) Citation

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

```bibtex
@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) 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) 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) Author / contact

**Mudasir** — multimodal AI, VLM fine-tuning, retrieval/RAG research, and applied NLP engineering; **MS AI Convergence**, [Soongsil University](https://ssu.ac.kr/), Seoul. More projects and publications: **[mudasir13cs.github.io](https://mudasir13cs.github.io/)**

- **Hugging Face:** [@mudasir13cs](https://huggingface.co/mudasir13cs)
- **GitHub:** [@mudasir13cs](https://github.com/mudasir13cs)
- **Email:** mudasir13cs@gmail.com

---

**Version:** 1.0 · **Last updated:** July 2026