@inproceedings{ling-etal-2026-shopperbench,
title = "{S}hopper{B}ench: A Benchmark for Personalized Shopping with Persona-Guided Simulation",
author = "Ling, Yuan and
Yuan, Chunqing and
Dong, Shujing and
Yang, Yongjian and
Mocherla, Nataraj and
Goyal, Ayush",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.62/",
pages = "837--846",
ISBN = "979-8-89176-384-5",
abstract = "Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and $\tau^2$-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference cues, capturing how diverse shopper types{---}from price-conscious planners to trend-seeking explorers{---}navigate product search and selection. We further design a baseline of ShopperAgents that operate under persona guidance to simulate realistic, goal-oriented shopping interactions. To evaluate these agents, we propose new metrics including Persona Fidelity, Persona-Query Alignment, and Path Consistency. Together, Our ShopperBench provides a testbed for studying personalized and context-aware shopping intelligence, bridging the gap between human-centered e-commerce behavior and agent-based simulation."
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<abstract>Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and τ²-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference cues, capturing how diverse shopper types—from price-conscious planners to trend-seeking explorers—navigate product search and selection. We further design a baseline of ShopperAgents that operate under persona guidance to simulate realistic, goal-oriented shopping interactions. To evaluate these agents, we propose new metrics including Persona Fidelity, Persona-Query Alignment, and Path Consistency. Together, Our ShopperBench provides a testbed for studying personalized and context-aware shopping intelligence, bridging the gap between human-centered e-commerce behavior and agent-based simulation.</abstract>
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%0 Conference Proceedings
%T ShopperBench: A Benchmark for Personalized Shopping with Persona-Guided Simulation
%A Ling, Yuan
%A Yuan, Chunqing
%A Dong, Shujing
%A Yang, Yongjian
%A Mocherla, Nataraj
%A Goyal, Ayush
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F ling-etal-2026-shopperbench
%X Personalized shopping agents must adapt their decisions to different user personas, balancing efficiency, preference alignment, and goal success. Building upon the WebShop dataset and τ²-Bench environment, ShopperBench introduces a persona-guided benchmark for evaluating such adaptive behaviors. ShopperBench augments shopping trajectories with persona-conditioned goals, reasoning rationales, and preference cues, capturing how diverse shopper types—from price-conscious planners to trend-seeking explorers—navigate product search and selection. We further design a baseline of ShopperAgents that operate under persona guidance to simulate realistic, goal-oriented shopping interactions. To evaluate these agents, we propose new metrics including Persona Fidelity, Persona-Query Alignment, and Path Consistency. Together, Our ShopperBench provides a testbed for studying personalized and context-aware shopping intelligence, bridging the gap between human-centered e-commerce behavior and agent-based simulation.
%U https://aclanthology.org/2026.eacl-industry.62/
%P 837-846
Markdown (Informal)
[ShopperBench: A Benchmark for Personalized Shopping with Persona-Guided Simulation](https://aclanthology.org/2026.eacl-industry.62/) (Ling et al., EACL 2026)
ACL