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NAVINES SEO LAB

调查

研究和数据具体说明方法、基本原理和局限性。

研究证据库

完整阅读六篇论文，并转化为有边界的产品决策。

arXiv · 2022-10-18

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

作者
Xinyu Zhang; Nandan Thakur; Odunayo Ogundepo; Ehsan Kamalloo; David Alfonso-Hermelo; Xiaoguang Li; Qun Liu; Mehdi Rezagholizadeh; Jimmy Lin
发布日期
2022-10-18
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有支持
  • 历史性
  • 仅学术

对NAVINES的影响: 多语言页面比较实验室

arXiv · 2024-04-14

Competitive Retrieval: Going Beyond the Single Query

作者
Haya Nachimovsky; Moshe Tennenholtz; Fiana Raiber; Oren Kurland
发布日期
2024-04-14
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有限
  • 仅学术

对NAVINES的影响: 竞争性多查询组合规划器

arXiv · 2020-05-26

Ranking-Incentivized Quality Preserving Content Modification

作者
Gregory Goren; Oren Kurland; Moshe Tennenholtz; Fiana Raiber
发布日期
2020-05-26
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有限
  • 仅学术

对NAVINES的影响: 竞争性多查询组合规划器

Journal of Artificial Intelligence Research · 2025

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

作者
Omer Madmon; Idan Pipano; Itamar Reinman; Moshe Tennenholtz
发布日期
2025
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有支持
  • 有限
  • 仅学术

对NAVINES的影响: 竞争性多查询组合规划器

KDD / arXiv · 2024

GEO: Generative Engine Optimization

作者
Pranjal Aggarwal; Vishvak Murahari; Tanmay Rajpurohit; Ashwin Kalyan; Kartik Narasimhan; Ameet Deshpande
发布日期
2024
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有限
  • 历史性
  • 仅学术

对NAVINES的影响: AI 引用准备实验室

arXiv · 2024-07-02

Adversarial Search Engine Optimization for Large Language Models

作者
Fredrik Nestaas; Edoardo Debenedetti; Florian Tramèr
发布日期
2024-07-02
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研究问题

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.

证据支持范围

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

局限与可迁移性

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.

主张分类

  • 有支持
  • 有限
  • 仅学术

对NAVINES的影响: AI 搜索操纵安全检查器

证据审核于 2026 年 8 月 14 日 · 不代表当前 Google 排名因素或保证结果。

调查

研究和数据具体说明方法、基本原理和局限性。

不要追逐算法。建立清晰、信任和累积可见性。

我们使高级搜索智能变得易于理解、有用且易于使用，帮助企业将其转化为可衡量的行动。

方法

  • 定义目标模板、用户和转化路径。
  • 示例响应、机器人、规范、hreflang、链接和结构化数据。
  • 在进行更改之前确定目标、关键指标、保护指标和目标细分。
  • 准确保存基准期和实施日期。

限制

  • Search Console 聚合包括舍入和匿名化。
  • 仅分析数据并不能证明因果关系。