{"ID":2837201,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20867","arxiv_id":"2511.20867","title":"E-GEO: A Testbed for Generative Engine Optimization in E-Commerce","abstract":"With the rise of large language models (LLMs), generative engines are becoming powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization (GEO)--improving content visibility and relevance for generative engines. Yet despite its growing importance, current GEO practices are ad hoc, and their impacts remain poorly understood, especially in e-commerce. We address this gap by introducing E-GEO, the first benchmark built specifically for e-commerce GEO. E-GEO contains over 7,000 realistic, multi-sentence consumer product queries paired with relevant listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets largely miss. Using this benchmark, we conduct the first large-scale empirical study of e-commerce GEO, evaluating 15 common rewriting heuristics and comparing their empirical performance. To move beyond heuristics, we further formulate GEO as a tractable optimization problem and develop a lightweight iterative prompt-optimization algorithm that can significantly outperform these baselines. Surprisingly, the optimized prompts reveal a stable, domain-agnostic pattern--suggesting the existence of a \"universally effective\" GEO strategy. Our data and code are publicly available at https://github.com/psbagga17/E-GEO.","short_abstract":"With the rise of large language models (LLMs), generative engines are becoming powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization...","url_abs":"https://arxiv.org/abs/2511.20867","url_pdf":"https://arxiv.org/pdf/2511.20867v1","authors":"[\"Puneet S. Bagga\",\"Vivek F. Farias\",\"Tamar Korkotashvili\",\"Tianyi Peng\",\"Yuhang Wu\"]","published":"2025-11-25T21:28:40Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":606668,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2837201,"paper_url":"https://arxiv.org/abs/2511.20867","paper_title":"E-GEO: A Testbed for Generative Engine Optimization in E-Commerce","repo_url":"https://github.com/psbagga17/E-GEO","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}