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

TL;DR

This paper introduces OPERA, a comprehensive dataset capturing human online shopping behavior, including actions, personas, observations, and rationales, to evaluate LLMs' ability to simulate individual user actions.

Contribution

The paper presents the first public dataset combining user personas, observations, actions, and rationales for online shopping, and establishes a benchmark for LLMs' predictive capabilities.

Findings

OPERA enables evaluation of LLMs in predicting user actions and rationales.

The dataset includes high-fidelity data collected via questionnaires and browser plugins.

A benchmark is established for assessing LLMs' performance in personalized behavior simulation.

Abstract

Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and <observation, action, rationale> history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.