Octoparse/ecommerce-visual-matching-dataset

E-commerce Visual Matching Dataset

Candidate match workflow dataset for product identity resolution, visual similarity, and review decision fields.

A public-safe workflow preview of how Octoparse structures AI-assisted product matching pipelines for e-commerce and pricing teams. Every row represents a candidate pair evaluation — the same structure delivered to production clients.

Built by [Octoparse Managed Data Service](https://www.octoparse.com/data-service/ai-visual-product-matching) — managed web data pipelines for pricing intelligence and catalog teams.

Public-safe proof, not raw client data. Source product IDs, retailer names, and image URLs have been masked or replaced with workflow tokens. This dataset is designed to show schema, field naming, QA context, and delivery format. Final production pipelines are scoped per customer and delivered under agreed terms.

Enterprise Data Pipelines & Production-Grade Delivery

This dataset is a curated, static sample provided by [Octoparse Managed Data Service](https://www.octoparse.com/data-service). If your organization requires: - Real-time automated updates: Daily/Hourly feeds via API, Snowflake, AWS S3, or BigQuery - Custom schema alignment and production-grade anti-bot operations with 99.9% SLA-backed delivery - Compliance-aware data delivery workflows for public or properly authorized data, with support for GDPR/CCPA-aligned requirements [Request a Custom Data Pipeline Workshop on Octoparse Data Service](https://www.octoparse.com/data-service)

Why This Dataset

Most product matching datasets available publicly are:

• —Flat and unstructured — raw scraped listings without match decisions or rejection reasons
• —Benchmark-only — built for model accuracy evaluation, not production workflow design
• —Missing QA context — no confidence scores, reject reasons, or review routing logic

This dataset is different:

• —Candidate-pair schema — each row is a source↔candidate evaluation, not a raw product listing
• —Match decisions included — match_status captures the final output: accepted, rejected, or needs review
• —Reject reasons structured — reject_reason shows why candidates were declined, enabling workflow analysis
• —Review notes present — review_note captures QA context for borderline cases

Dataset Structure

ecommerce-visual-matching-dataset/
├── visual_matching_workflow_sample.csv   ← Candidate-pair workflow rows
├── schema.json                           ← Machine-readable field definitions
├── data_dictionary.md                    ← Business context for every field
└── LICENSE                              ← CC BY-NC 4.0

Core Fields

match_status Values

reject_reason Categories

Quick Start

import pandas as pd

df = pd.read_csv("visual_matching_workflow_sample.csv")

# Output bucket distribution
print(df["match_status"].value_counts())

# Rejection reason breakdown
rejected = df[df["match_status"] == "rejected"]
print(rejected["reject_reason"].value_counts())

# Similarity score distribution by decision
print(df.groupby("match_status")["similarity_score"].describe())

# Review queue — candidates needing human judgment
review_queue = df[df["match_status"] == "needs_review"]
print(f"{len(review_queue)} candidates routed to review")

# Accepted matches with high confidence
high_confidence = df[(df["match_status"] == "accepted") & (df["similarity_score"] >= 0.85)]
print(f"{len(high_confidence)} high-confidence accepted matches")

What This Dataset Answers

What is AI visual product matching? Visual product matching is the process of determining whether products listed across different retailer, marketplace, or competitor sites refer to the same item — using image similarity, text signals, and structured QA to produce an explainable match decision.

Why does matching require more than image similarity? A high similarity_score alone is not sufficient. Products can look nearly identical but differ in model number, size, or spec. This workflow combines visual similarity with title validation and structured rejection reasons to produce decisions a pricing team can act on.

What does a production matching output look like? This dataset shows the candidate-pair output format: each row is a source↔candidate evaluation with a similarity score, a match decision, a rejection reason if applicable, and a review note for borderline cases. This is the data contract delivered to production clients.

How should downstream systems consume match decisions?

• —accepted rows feed directly into pricing intelligence or catalog deduplication
• —needs_review rows route to an analyst queue for QA
• —rejected rows with reject_reason can be used to tune retrieval thresholds

Use Cases

1. Competitor Price Monitoring

Use accepted matches to build a cross-retailer price comparison table. source_product_id maps your catalog; candidate_product_id maps competitor listings. Accepted rows give you the joined dataset for price delta analysis.

2. Catalog Deduplication

Use match_status = accepted + similarity_score to identify duplicate or equivalent listings in a multi-source product catalog. reject_reason = different_variant helps separate true duplicates from variant siblings.

3. Matching Pipeline Evaluation

Use the full candidate-pair schema to design or evaluate your own matching pipeline. The reject_reason distribution shows where retrieval methods fail and where visual scoring succeeds.

4. Review Queue Design

Use needs_review rows + review_note to design analyst escalation logic. The notes show what information a QA analyst needs to resolve low-confidence candidates.

5. AI Model Training Data Structure

Use the schema as a template for structuring your own training data. The similarity_score + match_status pairing provides a label structure for fine-tuning multimodal classifiers.

Data Limitations

Related Resources

• —Service page: AI Visual Product Matching Service
• —Automotive case study: Automotive Parts Matching — Extreme Dimensions
• —Retail case study: Retail Product Matching Workflow
• —Data Service hub + sample library: Octoparse Data Service
• —Paired Kaggle dataset: ecommerce-visual-matching-dataset on Kaggle

Want Production-Scale Visual Matching?

This is a sample dataset. If your team needs:

• —Cross-retailer product matching at scale (thousands of SKUs per day)
• —Automotive, furniture, electronics, or apparel catalog matching
• —Similarity scoring + QA review routing with structured rejection reasons
• —Delivery to Snowflake, BigQuery, or S3 in CSV, Parquet, or JSONL

→ [Talk to Octoparse Managed Data Service](https://www.octoparse.com/data-service/ai-visual-product-matching)

We scope the matching workflow, handle retrieval and scoring, and deliver reviewed outputs your pricing or catalog team can act on directly.

Citation

@dataset{octoparse_visual_matching_2025,
  title        = {E-commerce Visual Matching Dataset},
  author       = {{Octoparse Managed Data Service}},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/Octoparse/ecommerce-visual-matching-dataset},
  license      = {CC BY-NC 4.0},
  note         = {Public-safe candidate-pair workflow dataset for AI visual product matching and pricing intelligence}
}

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