Gaming PCs and Hardware Components Product Retrieval Dataset

#Product Document Retrieval #Query-Product Matching #Semantic Search #Relevance Evaluation #Gaming PC Shopping #Hardware Product Search #Budget Matching #Configuration Filtering
Records
5360
Size
0.5GB
Format
JSON
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-04
AI Analysis & Value Prop

Covers retail catalog documents for gaming desktops, graphics cards, processors, motherboards, memory, and storage devices, paired with search queries based on gaming performance, use cases, budgets, and hardware requirements. The data includes normalized product categories, brands, specifications, price bands, performance profiles, and query intents to support training and evaluation of models that retrieve relevant products from hardware catalogs.

Overview

Dataset Insights

Preview

Sample Examples

sample.json
{}

Schema

Technical Specifications

file_namestringFile name
price_bandstringBudget tier assigned based on the product price and catalog range.
query_intentobjectUse case, budget, hardware requirements, and performance preferences extracted from the search query.
search_querystringOriginal search text based on gaming performance, use case, budget, or hardware requirements.
retrieval_textstringDerived text that combines normalized product information and query intent for vector or keyword retrieval.
normalized_brandstringBrand name cleaned and standardized for capitalization and aliases from the product document.
product_documentobjectOriginal retail catalog document for a gaming desktop or hardware component.
matching_featuresarrayShared features between the product document and search query that can inform relevance judgments.
performance_profileobjectEstimated overall performance, target resolution, and primary use cases based on hardware specifications.
normalized_specificationsobjectKey hardware specifications extracted from the catalog document and standardized to consistent units.
normalized_product_categorystringProduct category normalized from the catalog document to align category labels across catalogs.

Trust

Compliance Statement

Authorization Type

Proprietary - Commercial AI Training License (No Redistribution)

Commercial Use

Requires exclusive subscription or authorization contract (monthly or per-invocation charging)

Privacy and Anonymization

No PII, no real company names, simulated scenarios follow industry standards

Compliance System

Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

FAQ

Frequently Asked Questions

What products does the dataset cover?
It covers gaming desktops, graphics cards, processors, motherboards, memory, and storage devices. The data pairs product catalog documents with search queries based on performance, use cases, budgets, and hardware requirements.
What normalized information is included?
Fields include normalized product categories, brands, and hardware specifications, as well as price bands, performance profiles, query intents, matching features, and retrieval text. These fields can support end-to-end search and evaluation workflows.
Which model tasks can this dataset support?
It supports product document retrieval, query-to-product matching, semantic or keyword search, and retrieval relevance evaluation. It can also help assess how well product configurations match budget and hardware requirements.
How can I access the data, and what are the licensing terms?
Refer to the download or delivery options on the product page, and review the stated license scope, usage restrictions, and update information before use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Gaming PCs and Hardware Components Product Retrieval Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/be66792fc2457615791193860fb8efb9?cate=3},
  urldate={2026-10-28},
  keywords={gaming PC dataset,hardware product retrieval,gaming desktop catalog,graphics card search,hardware configuration matching,product document retrieval,PC shopping queries,retail catalog data},
  version={1.0}
}

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