I'm building an internal search ranking evaluator and need a benchmark dataset

A good benchmark dataset for an internal search ranking evaluator should include more than just documents and queries — it should also have relevance judgments or implicit feedback so you can measure ranking quality reliably.

What your benchmark should contain

At minimum:

• Query text
• Candidate documents/items
• Relevance labels
  • ideally graded labels like 0 = irrelevant, 1 = relevant, 2 = highly relevant
• Metadata
  • language, product/category, timestamp, query intent, source, etc.
• Split information
  • train / validation / test, or at least a held-out test set

Good public benchmark options

Depending on your domain:

General web / retrieval

• MS MARCO
  • widely used for passage/document ranking
  • good for learned ranking and retrieval evaluation
• TREC datasets
  • strong evaluation tradition, often with graded judgments
• BEIR
  • a benchmark suite across many retrieval tasks/domains
  • useful if you want robustness checks

Search / recommendation / e-commerce style

• MIND
  • news recommendation, but has query/candidate ranking characteristics
• Amazon product review / recommendation datasets
  • useful if your internal search is product-oriented, though not a direct search benchmark
• KDD Cup / CIKM search-related datasets
  • sometimes closer to vertical search or sponsored ranking scenarios

Semantic matching / ranking

• Quora Question Pairs
  • more for duplicate detection / query similarity than search ranking
• Natural Questions
  • good if your internal search is QA-like

If you’re evaluating internal search specifically

The best benchmark is usually a company-specific labeled dataset built from:

• historical search queries
• clicked documents
• dwell time / reformulation signals
• editorial judgments for a sampled subset

This is often better than any public dataset because it matches:

• your users
• your content corpus
• your query patterns
• your business goals

Recommended evaluation metrics

For ranking:

• NDCG@k
• MRR@k
• MAP
• Recall@k
• Precision@k

If you have graded relevance, NDCG is usually the most informative.

Practical benchmark design

A solid internal benchmark setup:

1. Collect a few thousand real queries
2. Sample top retrieved results from your current system
3. Get human labels from domain experts
4. Stratify by query type:
   • navigational
   • informational
   • transactional
   • long-tail / ambiguous
5. Freeze a test set and refresh it periodically

If you want, I can help with one of these next:

• recommend a dataset based on your domain
• design a benchmark schema
• draft a labeling guideline
• give you an evaluation script for NDCG/MRR/Recall
• propose a data collection pipeline for internal search logs