Pith Number

pith:FRKQKJMG

pith:2025:FRKQKJMGCK5VWQENNM2GTHG4NI

not attested not anchored not stored refs pending

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation

Amirali Amini, Bo Sun, Dakuo Wang, Jing Huang, Jiri Gesi, Lydia Chilton, Malihe Alikhani, Tian Wang, Toby Jia-Jun Li, Upol Ehsan, Weimin Lyu, Wenbo Li, Yakov Bart, Yu Su, Yuxuan Lu, Ziyi Wang

The OPERA dataset records real shoppers' observations, personas, rationales, and actions to test LLMs' ability to simulate individual human online behavior.

arxiv:2506.05606 v7 · 2025-06-05 · cs.CL · cs.HC

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

OPERA is the first public dataset that comprehensively captures user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales from real human participants during online shopping sessions.

C2weakest assumption

That participants' self-reported rationales collected via the questionnaire accurately reflect their internal reasoning at the moment of action without distortion from the act of reporting or the presence of the browser plugin.

C3one line summary

OPERA is the first public dataset pairing user personas, live browser observations, fine-grained web actions, and immediate self-reported rationales from real online shopping sessions to benchmark LLM prediction of individual human behavior.

Formal links

1 machine-checked theorem link

Cited by

4 papers in Pith

ShopGym: An Integrated Framework for Realistic Simulation and Scalable Benchmarking of E-Commerce Web Agents

SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents

SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents

Receipt and verification

First computed	2026-05-20T00:04:10.504555Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2c5505258612bb5b408d6b34699cdc6a1fb3554c0dfb8528997aea6c5b201cf1

Aliases

arxiv: 2506.05606 · arxiv_version: 2506.05606v7 · doi: 10.48550/arxiv.2506.05606 · pith_short_12: FRKQKJMGCK5V · pith_short_16: FRKQKJMGCK5VWQEN · pith_short_8: FRKQKJMG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FRKQKJMGCK5VWQENNM2GTHG4NI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2c5505258612bb5b408d6b34699cdc6a1fb3554c0dfb8528997aea6c5b201cf1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "76ec5f3d7178fc63efb74e7a48ab26730f0655eaed9a2fdd34079f78bdc1a10e",
    "cross_cats_sorted": [
      "cs.HC"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-06-05T21:37:49Z",
    "title_canon_sha256": "ee1c65828a894a6e571ed2dbbe234fd3a8693a972da09d8ca50de9c634462dce"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 7
  }
}