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.

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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.

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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)

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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:

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

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How ERESS Was Constructed (Overview)

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

1. 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.

2. 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.

3. 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.

4. 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

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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)

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Usage

Loading

from datasets import load_dataset

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