@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://preview.aclanthology.org/ingest-eacl/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."
}