---
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id: news-20260720-464bff
url: https://arxiv.org/abs/2311.09735
url_hash: 464bffcc78ad5f4cc1108986332cad704b7b69320af4fd20a32a0586ce539c82
canonical_url: https://arxiv.org/abs/2311.09735
source: arxiv.org
category: news/tech
category_raw: it_ai
region: null
tags: []
lang: null
published_at: null
fetched_at: '2026-07-20T12:38:31.875416Z'
updated_at: '2026-07-20T12:38:50Z'
status: published
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license_note: link-only
summary: null
summary_source: null
summary_en: null
entities:
- name: GEO
  type: concept
- name: Generative AI
  type: UNKNOWN
- name: ex-engineer
  type: person
- name: optimization_technology
  type: UNKNOWN
- name: arXiv
  type: UNKNOWN
- name: Researchers of Princeton University and related institutions,Event
  type: UNKNOWN
- name: tech
  type: UNKNOWN
key_facts: []
related: []
related_auto:
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  type: concept
  weight: 1.0
- name: SEO
  type: method
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- name: ゼロクリック検索
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- name: Researchers of Princeton University and related institutions,Method
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title: '学術論文『GEO: Generative Engine Optimization』（arXiv:2311.09735 / Princeton University,
  Georgia Tech）'
---

# 学術論文『GEO: Generative Engine Optimization』（arXiv:2311.09735 / Princeton University, Georgia Tech）

## TL;DR
（要約なし・原文はリンク先を参照 / No summary available; see the source link.）

## Key Points
- (なし)

## Details
(本文なし。リンク先参照)

## Source
元記事: [学術論文『GEO: Generative Engine Optimization』（arXiv:2311.09735 / Princeton University, Georgia Tech）](https://arxiv.org/abs/2311.09735)