# E-GEO: A Testbed for Generative Engine Optimization in E-Commerce

arXiv: https://arxiv.org/abs/2511.20867 · HTML: https://mlsanity.com/paper/2511.20867/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2025.

## Built on by (most cited first, 8 of 8)

- [SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents](https://mlsanity.com/paper/2606.28356/) (2026)
- [IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization](https://mlsanity.com/paper/2601.13938/) (2026)
- [EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents](https://mlsanity.com/paper/2605.12887/) (2026)
- [Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems](https://mlsanity.com/paper/2606.17443/) (2026)
- [GEO-Flag: Detecting and Measuring GEO-Optimized Web Content](https://mlsanity.com/paper/2608.16824/) (2026)
- [Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes](https://mlsanity.com/paper/2608.11390/) (2026)
- ["If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations](https://mlsanity.com/paper/2609.18729/) (2026)
- [LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses](https://mlsanity.com/paper/2609.32533/) (2026)

## Abstract (arXiv)

With the rise of large language models (LLMs), generative engines have become 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. Despite its growing importance, current GEO practices are largely ad hoc, and their impacts remain poorly understood, especially in the e-commerce setting. We address this gap by introducing E-GEO, the first dataset built specifically for e-commerce GEO. E-GEO contains 13,747 realistic, multi-sentence consumer product queries, each paired with 10 retrieved Amazon listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets miss. Using this dataset, we conduct the first large-scale empirical study of e-commerce GEO across five representative generative engines, seven popular LLM rewriters, and fifteen hand-crafted rewriting heuristics. We further formulate GEO as an optimization problem and develop a lightweight prompt meta-optimization algorithm that significantly improves over heuristic baselines. Notably, the optimized prompts reveal a stable, domain-agnostic pattern, suggesting the existence of a "universally effective" GEO strategy. Finally, we red-team the GEO system through both heuristic and optimization-based attacks and show that, under a simple in-prompt defense, gains from GEO reflect genuine content improvement rather than manipulation, anchoring GEO as a substantive and well-defined optimization problem.