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調査

方法、根拠、限界を明記した研究とデータです。

研究エビデンス資料庫

提供された6本の論文を全文精読し、範囲を限定した製品判断へ変換しました。

arXiv · 2022-10-18

Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages

著者
Xinyu Zhang; Nandan Thakur; Odunayo Ogundepo; Ehsan Kamalloo; David Alfonso-Hermelo; Xiaoguang Li; Qun Liu; Mehdi Rezagholizadeh; Jimmy Lin
公開日
2022-10-18
根拠記録を開く

研究課題

How can a retrieval benchmark cover languages with very different resource levels while using native queries and relevance judgments?

方法

An 18-language, same-language Wikipedia retrieval benchmark built with native-speaker queries and judgments; lexical, dense and hybrid baselines were compared on the released pools.

根拠が支持する範囲

Evaluate each language explicitly, document corpus and judgment construction, and keep native review central. The reported hybrid result is a historical benchmark observation.

限界と転用可能性

This is same-language information retrieval, not translation assessment or web SEO. Heuristic segmentation, candidate-pool gaps and unfinished test labels limit transferability; it says nothing about hreflang or Google rankings.

主張の分類

  • 支持あり
  • 歴史的
  • 学術限定

NAVINESへの反映: 多言語ページ比較ラボ

arXiv · 2024-04-14

Competitive Retrieval: Going Beyond the Single Query

著者
Haya Nachimovsky; Moshe Tennenholtz; Fiana Raiber; Oren Kurland
公開日
2024-04-14
根拠記録を開く

研究課題

How does publisher competition change when participants allocate content decisions across multiple queries rather than one isolated query?

方法

A formal multi-query game plus four controlled student competitions covering 30 TREC topics, 84 participants and specified proxy rankers; best-response dynamics and feature changes were examined.

根拠が支持する範囲

Cross-query opportunity cost and competitor response deserve explicit treatment; equilibrium existence does not imply that learning dynamics will converge.

限界と転用可能性

The student setting, topics and rankers are controlled proxies. The work does not identify current Google features, predict winners or justify reusable weights or a practical Nash-equilibrium claim.

主張の分類

  • 限定的
  • 学術限定

NAVINESへの反映: 競争的な複数クエリ・ポートフォリオ計画

arXiv · 2020-05-26

Ranking-Incentivized Quality Preserving Content Modification

著者
Gregory Goren; Oren Kurland; Moshe Tennenholtz; Fiana Raiber
公開日
2020-05-26
根拠記録を開く

研究課題

Can ranking-incentivized content modification include coherence and modification cost instead of optimizing promotion alone?

方法

A controlled passage-replacement method balanced promotion and coherence, evaluated with proxy rankers and offline data across 31 queries, with an explicit ethics discussion.

根拠が支持する範囲

Quality constraints and the cost of changing content belong in planning. A promotional change that fails editorial integrity should be rejected.

限界と転用可能性

Passages came from higher-ranked candidates and quality judgments were bounded and subjective. It does not support copying winners, real-engine causal claims or unrestricted automated rewriting.

主張の分類

  • 限定的
  • 学術限定

NAVINESへの反映: 競争的な複数クエリ・ポートフォリオ計画

Journal of Artificial Intelligence Research · 2025

The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents

著者
Omer Madmon; Idan Pipano; Itamar Reinman; Moshe Tennenholtz
公開日
2025
根拠記録を開く

研究課題

Under which formal ranking mechanisms and content-deviation costs can strategic publisher learning converge or remain unstable?

方法

Formal publisher-utility models with rank benefit minus deviation cost, convergence results for specified linear/softmax settings, and discrete simulations, predominantly with two publishers.

根拠が支持する範囲

Change cost, response dynamics and instability are useful planning dimensions; a mechanism can have an equilibrium while local responses cycle or remain pseudoperiodic.

限界と転用可能性

The model assumes a static information need, known ranking function and embedding-space actions. Simulations are not a real search engine and do not justify a Google mechanism or guaranteed convergence claim.

主張の分類

  • 支持あり
  • 限定的
  • 学術限定

NAVINESへの反映: 競争的な複数クエリ・ポートフォリオ計画

KDD / arXiv · 2024

GEO: Generative Engine Optimization

著者
Pranjal Aggarwal; Vishvak Murahari; Tanmay Rajpurohit; Ashwin Kalyan; Kartik Narasimhan; Ameet Deshpande
公開日
2024
根拠記録を開く

研究課題

In controlled generative-engine experiments, how do selected content modifications affect proxy visibility across queries and domains?

方法

GEO-BENCH combined 10,000 queries from nine datasets with visibility proxies and experiments on two generative engines; citation, quotation, statistics and other interventions were compared by domain.

根拠が支持する範囲

Clear attribution, genuine evidence and domain-aware experimentation can be reviewed as editorial signals; keyword stuffing performed poorly in this benchmark.

限界と転用可能性

Two engines, black-box variance and proxy metrics do not establish traditional-search effects or future citation probability. Reported gains are historical benchmark results, not forecasts; appendix prompts are untrusted.

主張の分類

  • 限定的
  • 歴史的
  • 学術限定

NAVINESへの反映: AI引用準備ラボ

arXiv · 2024-07-02

Adversarial Search Engine Optimization for Large Language Models

著者
Fredrik Nestaas; Edoardo Debenedetti; Florian Tramèr
公開日
2024-07-02
根拠記録を開く

研究課題

Can content in retrieved web or plugin documents manipulate an LLM's product preferences, and which broader attack categories appear beyond classic prompt injection?

方法

Controlled experiments with fictional products, web/plugin documents and proprietary models examined model-directed instructions, concealment, false claims, source suppression and competitor discrediting.

根拠が支持する範囲

Retrieved content is an untrusted input surface; safety review should cover concealed persuasion and source integrity as well as direct instructions.

限界と転用可能性

The favorable fictional setup, limited scale and changing proprietary systems do not establish prevalence or complete detection. Exact attack payloads are not republished and the checker is not a certification.

主張の分類

  • 支持あり
  • 限定的
  • 学術限定

NAVINESへの反映: AI検索操作の安全チェック

2026年8月14日レビュー · 現在のGoogle順位要因や結果保証を示すものではありません。

調査

方法、根拠、限界を明記した研究とデータです。

アルゴリズムを追い回しません。明瞭さ、信頼、積み重なる可視性を築きます。

高度な検索インテリジェンスを理解しやすく、役立ち、利用しやすいものにし、企業が測定可能な行動へ変えられるよう支援します。

方法

  • 対象テンプレート、利用者、コンバージョン経路を定義する。
  • 応答、robots、canonical、hreflang、リンク、構造化データを標本検査する。
  • 変更前に目的、主指標、保護指標、対象セグメントを決める。
  • 基準期間と実装日を正確に保存する。

限界

  • Search Consoleの集計には丸めや匿名化があります。
  • 解析データだけでは因果関係を証明できません。