Datasets:

thebajajra
/

eress

license: apache-2.0
language:
  - en
pretty_name: E-commerce Relevance Evaluation Scoring Suite
tags:
  - e-commerce
  - information-retrieval
  - reranking
  - ranking
  - evaluation
  - search
  - relevance
  - ndcg
  - product-search
task_categories:
  - text-classification
  - sentence-similarity
size_categories:
  - 10K<n<100K

ERESS: E-commerce Relevance Evaluation Scoring Suite

Dataset Summary

ERESS (E-commerce Relevance Evaluation Scoring Suite) is an evaluation dataset for product discovery / e-commerce search reranking. It contains ~4.7k unique queries and ~72k labeled (query, product) pairs, with graded relevance designed to reflect real shopping behavior and modern “assistant-style” query intent (e.g., utility/task framing, gift/audience constraints, attribute-heavy queries).

ERESS is built to stress-test rerankers under common real-world failure modes:

Hard negatives (semantically close but wrong items)
Lexical confounders (high token overlap, wrong intent)
Near-substitutes (plausible alternatives that violate a key constraint like size/color/compatibility)

The dataset is intended primarily for holistic evaluation of reranking models using nDCG (e.g., nDCG@5, nDCG@10) with graded relevance.

Motivation

Public relevance sets such as Amazon-ESCI and WANDS can under-represent modern e-commerce traffic, especially:

Gift and audience-specific constraints (“gift for…”, “for my dad who…”, occasions)
Utility/task queries (“fix squeaky door”, “organize cables”, “reduce glare”)
Highly attribute-constrained queries and conversational phrasing

ERESS is explicitly stratified to better cover these intents and to include harder confounders that survive high-recall retrieval.

Supported Tasks and Leaderboard-Style Use

Primary: Reranking / relevance estimation for product search

Input: a query and a candidate product (title/description/attributes)
Output: a relevance score (graded)

Recommended metrics:

nDCG@k (primary; graded relevance)
Optionally: MRR@k or Precision@k (secondary)

Dataset Composition

High-level stats

Unique queries: ~4.7k
Labeled pairs: ~72k (query, product)
Labels: graded relevance (scalar in [0, 1] as described in the accompanying project materials)

Query intent families

ERESS queries are designed to reflect a spectrum of shopping intents, including:

Attribute-rich (high feature density; size/material/compatibility constraints)
Navigational (brand/store/product-line seeking)
Gift & audience-specific (recipient + occasion)
Generic (category-level intent with minimal attributes)
Utility (task/solution framing)
Short & Books (very short head queries + targeted books)

How ERESS Was Constructed (Overview)

ERESS is derived from a modern e-commerce relevance pipeline built for high-recall retrieval + reranking evaluation:

Query generation & coverage control
- Synthetic queries are generated under a constrained protocol and stratified across intent families.
- Queries are embedded and clustered to avoid over-representing paraphrases and to preserve semantic diversity.
High-recall candidate retrieval
- Candidates are retrieved via a dual-index strategy to improve recall across query types:
  - Index A: title-focused view (better for short/navigational)
  - Index B: full-text view (better for attribute-rich/utility)
- Candidate sets are merged and deduplicated per query.
Graded relevance annotation
- Each (query, product) pair is labeled using an ensemble (“council”) of LLMs.
- Annotation uses structured prompts that emit a discrete label as the first token; first-token logits are used as calibrated signals for scoring.
Quality control & leakage prevention
- Candidate-level pruning to remove trivial irrelevance
- Deduplication across splits (no duplicate query strings and no duplicate product IDs leaking across train/eval regimes)
- Semantic decontamination to reduce benchmark/train overlap via embedding similarity thresholds

Dataset Splits

ERESS is intended as an evaluation suite. If the hosted dataset includes splits, they typically correspond to evaluation partitions (e.g., test and/or multiple slices). If you create your own splits, ensure:

no duplicate queries across splits
no duplicate product IDs across splits (when evaluating generalization)
avoid semantic contamination between training and ERESS queries (nearest-neighbor overlap)

Usage

Loading

from datasets import load_dataset

ds = load_dataset("thebajajra/eress")
print(ds)
print(ds[list(ds.keys())[0]][0])