POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search

POISS is a benchmark for point-of-interest (POI) search. Given a natural-language query and the coordinate it was issued from, a system ranks POIs by relevance. Relevance here is genuinely two-sided: "coffee shops near me" depends on where the user is, while "hotel caribe en mérida yucatán" names a specific place hundreds of kilometres away. Models have to weigh semantics and geography together.

The dataset contains 309,386 queries in 7 languages across 14 locales, grounded in the Overture Maps Places corpus of more than 72 million POIs. Each query comes with a ranked list of candidate POIs (~301) with graded relevance labels, so the same data supports both retrieval over the full corpus and reranking of a candidate pool.

Fine-tuned baselines: amazon/poiss-bge-m3-retriever and amazon/poiss-bge-m3-reranker.

What you can build with it

• Geo-aware retrieval at scale. Retrieve from a 72M-POI index using the query text and the user's coordinate. Recall@{10,100,1000} and NDCG@{5,20} against graded labels.
• Reranking with graded relevance. Up to 20 ranked positives per query let you study fine-grained ordering, not just relevant/non-relevant.
• Multilingual and cross-lingual behaviour. English, Spanish, French, Portuguese, German, Italian and Dutch, each with enough test queries to report per-language numbers.
• Intent analysis. Queries split into name-anchored (Search, Detail) and open-ended (Recommend, Things-to-do) intents, which behave very differently: open-ended queries stay substantially harder for every system we measured.
• Geography versus semantics. Every query carries its issuing coordinate and every candidate its location, so distance can be modelled explicitly, combined with lexical or dense scores, or studied as a feature.
• Training data for retrievers and rerankers. The ranked, labeled candidate list per query is designed to be sampled from: positives, graded positives, judged negatives and an unjudged tail are all identified, so you can pick the positive/negative scheme your setup needs.

Dataset at a glance

	train	test
Queries	267,368	42,018
Candidate query–POI pairs	80,495,030	12,655,117
Candidates per query (avg)	301.1	301.2
Graded relevant POIs per query (avg)	14.15	13.78

Across both splits the dataset references 20,960,591 distinct Overture POIs. Queries average 3.26 tokens.

Row schema

Field	Type	Description
query_id	string	uuid4
split	string	train or test
query_text	string	the query
user_geolocation	struct{lat,lng: float64}	coordinate the query was issued from
candidates	list[string]	ordered Overture GERS identifiers
candidate_labels	list[string]	parallel to candidates, one label each
relevant_candidates	list[string]	the graded-relevant subset (≤ 20), in ranked order
consolidation_removed	list[string]	POIs identified as cross-source duplicates
label_counts	struct of 5 int64	count per label

Candidate ordering and labels

candidate_labels[i] describes candidates[i], and the labels form contiguous blocks in this order:

Order	Label	Meaning	avg/query (train)
1	relevant	graded positives, ranked	14.15
2	semantic_positive	judged relevant, beyond the graded top-20	6.45
3	semantic_negative_google	reference-engine candidate judged non-relevant	2.34
4	semantic_negative_poi_search_system	retrieved candidate judged non-relevant	11.14
5	unjudged_poi_search_system	retrieve-and-rerank tail	266.98

Because the order is fixed, a rank-based slice of candidates is also a label-based one: relevant_candidates is the head of the list, and label_counts gives the block boundaries in constant time.

Loading

from datasets import load_dataset

poiss = load_dataset("amazon/poiss")
row = poiss["test"][0]
print(row["query_text"], row["user_geolocation"])
print(row["candidates"][:5], row["candidate_labels"][:5])

Recovering POI content

POISS ships Overture identifiers; POI names, addresses, coordinates and categories come from Overture itself. scripts/join_overture.py does the join against a current release:

python scripts/join_overture.py --split test --release 2026-08-19.0 --out test_pois.parquet

Output columns: poiss_candidate_id, overture_id, names, addresses, categories, confidence, latitude, longitude, quality_score. Requires pyarrow and tqdm.

POISS was built on Overture release 2026-03-18, and Overture reassigns some GERS identifiers when it re-conflates the corpus. data/overture_id_map.parquet translates 1,853,120 such identifiers to their current equivalent, matched through the provider records the two releases share. With the map applied, 88.5% of candidate identifiers and about 90% of the graded positives resolve against release 2026-08-19; the remainder are POIs Overture has since retired, and the script lists them in its report. The map is refreshed as new Overture releases appear, and --id-map accepts your own.

Quality score

data/poi_quality_score.parquet maps overture_id to a score in [0, 5]: an LLM-derived quality signal used during labeling and read by the cross-encoder as its Score feature. It is not an Overture field, so it ships with the dataset.

import pyarrow.parquet as pq
table = pq.read_table("data/poi_quality_score.parquet")
scores = dict(zip(table.column("overture_id").to_pylist(), table.column("score").to_pylist()))

The file covers the POIs for which upstream rating information was available — 30.7% of query–candidate pairs, and 43.7% of the graded positives. Models were trained and evaluated with the field absent where no score exists.

Tasks and reference results

Full-corpus retrieval. The search space is the entire Overture corpus; systems are scored against the graded labels. Test split, percentages:

System	R@10	R@100	R@1k	N@5	N@20
Distance only	0.6	2.4	10.2	0.5	0.7
BM25	17.8	29.9	42.8	16.0	18.0
BM25 + distance	18.4	31.2	42.8	17.1	19.1
BGE-M3 zero-shot	15.2	26.4	40.0	13.7	15.3
BGE-M3 fine-tuned	45.2	83.2	95.6	58.3	60.3
Qwen3-Embedding-0.6B zero-shot	22.2	35.3	49.7	22.6	24.1
Qwen3-Embedding-0.6B fine-tuned	45.0	82.5	94.7	58.7	60.7

Fine-tuned per-language R@100 spans 79.8 (NL) to 86.1 (DE); per-intent R@100 is 89.1 on Search against 77.3 on Recommend.

Candidate reranking. A system reorders a per-query pool. The numbers below use the pool from the reference setup — the fine-tuned retriever's top-1000 over the full corpus — which can be rebuilt with the released retriever. Reranking the candidates column is also a valid task, in an easier setting where every graded positive is present by construction.

System	P@5	P@20	MRR	N@5	N@20
BGE-M3 retriever (bi-encoder order)	57.6	35.5	85.5	61.5	62.6
BGE-Reranker-v2-m3 zero-shot	24.6	14.3	50.0	32.1	34.8
BGE-Reranker-v2-m3 fine-tuned	63.0	38.3	89.7	66.8	66.6
Qwen3-Reranker-0.6B zero-shot	38.3	21.3	68.3	43.8	44.6
Qwen3-Reranker-0.6B fine-tuned	61.8	37.4	88.8	65.7	65.7

For reference, the reranker baseline was trained with positives = relevant and negatives drawn from semantic_negative_poi_search_system + unjudged_poi_search_system, padded to 100 per query. Other sampling schemes, or training on a rank-based subset of the full list, are equally available.

License and attribution

POISS is released under the Apache License 2.0, covering the queries, the relevance labels and their ordering, the identifier map, the quality scores and the scripts.

POI content is not redistributed here. When you join against Overture you are bound by Overture's terms: the places theme is published under CDLA-Permissive-2.0 and Apache-2.0 depending on the source provider and contains no OpenStreetMap data. Attribute Overture Maps Foundation and the per-source licenses accordingly; each Overture record carries its own sources[].license. See Overture licensing.

The released baselines carry the license of the model they were fine-tuned from: poiss-bge-m3-retriever is MIT, from BAAI/bge-m3; poiss-bge-m3-reranker is Apache-2.0, from BAAI/bge-reranker-v2-m3.

Citation

@inproceedings{maritan-etal-2026-poiss,
    title = "{POISS}: A Large-Scale Multilingual Dataset for Point-of-Interest Search",
    author = "Maritan, Nicola  and
      Moschitti, Alessandro  and
      Borazio, Federico  and
      Zhou, Xiaokun  and
      Bai, Zhengwei",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    note = "To appear",
}