Help me find a benchmark dataset for evaluating ranking models, with clear metrics and usage rights.

Here are solid benchmark dataset options for evaluating ranking models, with notes on metrics and usage rights. I’ll bias toward datasets that are widely used, easy to cite, and suitable for IR / learning-to-rank / retrieval evaluation.

Good benchmark choices

1) MS MARCO Passage Ranking

• Use case: Neural retrieval / passage ranking.
• What it contains: Real user queries paired with relevant passages.
• Common metrics: MRR@10, Recall@k, nDCG@10.
• Why it’s popular: Very common benchmark for ranking and retrieval; lots of published baselines.
• Usage rights: Available through Microsoft with a dataset license/terms of use; generally free for research use, but you should review the current license page before redistribution or commercial use.

2) TREC Deep Learning (TREC DL)

• Use case: Ad hoc document/passsage ranking.
• What it contains: Query sets with graded relevance judgments derived for deep learning retrieval evaluation.
• Common metrics: nDCG@10, MRR@10, MAP depending on the track/setup.
• Why it’s good: Strong evaluation quality due to human judgments.
• Usage rights: Distributed under TREC/NIST terms; often usable for research, but check the specific track and judgment files for restrictions.

3) LETOR / MSLR-WEB10K / MSLR-WEB30K

• Use case: Classic learning-to-rank benchmarks.
• What it contains: Query-document feature vectors with relevance labels.
• Common metrics: NDCG@k, MAP, ERR.
• Why it’s good: Standard for ranking model research and feature-based LTR.
• Usage rights: Publicly available from Microsoft Research with terms for research use; verify the current distribution/license page.

4) Yahoo! Learning to Rank Challenge Dataset

• Use case: Learning-to-rank experiments.
• What it contains: Preprocessed query-document feature data and labels.
• Common metrics: NDCG@k, MAP.
• Why it’s good: Simple and widely used in academic comparisons.
• Usage rights: Available for academic research under the dataset’s terms; check the original Yahoo research terms before reuse.

5) BEIR benchmark

• Use case: Broad retrieval/ranking evaluation across many tasks.
• What it contains: A collection of IR datasets spanning diverse domains.
• Common metrics: Usually nDCG@10, Recall@k, MAP, MRR@10 depending on task.
• Why it’s good: Good if you want a more general benchmark rather than a single dataset.
• Usage rights: Each component dataset has its own license/terms; BEIR itself is a benchmark framework, so rights depend on the underlying datasets.

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Which one should you pick?

• If you want a modern neural ranking benchmark: MS MARCO
• If you want stronger human relevance judgments: TREC DL
• If you want classic LTR with feature vectors: MSLR-WEB10K/30K
• If you want diverse retrieval tasks: BEIR

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Clear metrics to use

For ranking models, the most standard metrics are:

• NDCG@k: best for graded relevance and top-heavy ranking quality
• MRR@k: useful when there is usually one highly relevant item
• Recall@k: measures coverage of relevant items
• MAP: common in older IR/LTR setups, good for binary relevance
• ERR@k: sometimes used when you care about user satisfaction modeling

If you need one default:

• Use nDCG@10 for general ranking quality.
• Use MRR@10 if you care about finding the single best match quickly.

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Practical recommendation

If your goal is a benchmark with:

• clear, standard metrics
• widely recognized evaluation protocol
• reasonable usage clarity

I’d recommend:

1. MS MARCO for passage ranking
2. TREC DL for a stronger evaluation signal
3. MSLR-WEB30K if you want classic LTR with feature inputs

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Usage-rights caution

Before using any dataset:

• Check whether the dataset is research-only or allows commercial use
• Confirm if you may redistribute the data
• Check whether the dataset contains text from third-party sources, which may impose additional restrictions
• Prefer datasets with a clearly stated license or terms of use page

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If you want, I can also give you:

1. a shortlist of datasets with direct download links and license summaries, or
2. a recommendation based on your exact ranking task (search, recommendations, ads, LTR, LLM reranking, etc.).