Author: Olaf Kopp , 09.March 2023
Reading time: 4 Minutes

Relevance, pertinence and quality in search engines

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To the basics in Information Retrieval, so the science behind search engines , belong the terms relevance and pertinence. There is also the area Quality. In this article I would like to explain these terms / areas and differentiate them from each other.

What is relevance ?

Something is relevant for search engines if a document or content is significant in relation to the search query. The search query describes the situation and context. Google determines this relevance via text analysis, e.g. WDF*IDF or.TF-IDF as well as vector space analyses such as. Word2Vec. There are always two sides to relevance. The search query that expresses a search intention and the content that must match this search intention. Relevance is objective, i.e. independent of the individual needs of the individual user.

What is pertinence ?

Pertinence describes the subjective importance of a document for the user. This means that in addition to the match with the search query, there is also a subjective user level. In other words, a document can be interesting for one user with the same search query, but not for the other user. So a SEO-Professional can have a different expectation when he search the term search engine optimization as a beginner. While a beginner does not yet know many technical terms, a SEO professional is familiar with them.

Relevance, Pertinence and Usefulness in relation to the contextual levels

Whether something is perceived as relevant or meaningful depends on general context levels. For this I created the model of the context cube:

What ?

The question of what? should be considered with a view to objective relevance. This is about the content of the document.

Who? How ? Where ?

The answer to the question who? answers the question about the target group. This is important for pertinence. Likewise the questions about where? and how ?. Creating content for a beginner is different than for a professional. And a beginner’s text is assumed to be in a different place than a professional text.

Context levels that, according to Google, are included in the ranking

  • The place
  • The language
  • The time
  • The platform used
  • The distribution to different data centers

The difficulty in terms of pertinence and usefulness for Google

In terms of individual pertinence / usefulness, however, Google also tries to order documents according to the individual context of the user. However, it is not easy to balance the tension between diversity and individuality. In recent years, Google has worked a lot on personalizing the search results (e.g. according to search and click behavior or interactions with social contacts on Google+). Similar to the Facebook algorithm, justified criticism was repeatedly voiced that too much personalization of the output resulted in a one-sided presentation of facts. The risk of the “filter bubble” is all the greater the more individualized or personalized an output or search result is.

That may be a reason why Google restricted the personalization of the search results. With this, Google compiles the search results more in terms of relevance than pertinence.

Quality as a subject-related and site-wide evaluation level

The quality is a rating level in relation to an entity such as the publisher or author or the associated domains and documents in the aggregate. A typical example is the Panda or Coati System or the Helpful Content System, which refers to a quantity n of documents on a specific topical area or page area. The situation is similar with E-E-A-T and Page Experience. All of these systems work as a layer, assessing quality at the site level, domain level, and entity level.


Relevance, pertinence and quality on Google

At various points in the information retrieval process, Google tries to analyze information that enables search results to be output in terms of relevance, pertinence, usefulness and quality. About the query processing, Google tries the objective meaning as well as individual search intent  to identify the user.

The scoring engine is only responsible for the relevance scoring of the documents with regard to the search query. So only in terms of relevance. The scoring is based on classic ranking factors such as keywords in the page title and text, TF-IDF, any user signals related to the documents …

The quality assessment takes place via site-wide and topical layers. E-E-A-T, page experience, site-wide content quality… play a role here. This rating is not directly dependent on the search query, if so on topics.

The Personalization, which Google before delivery of the SERPs reapplies final should work in terms of pertinence and usefulness. Factors such as the search history (see the post Search history to identify the user context) or the location can be included in the individual assessment.

More on how Google works in the article How does Google (ranking) work today?


In the triad of relevance, pertinence and quality, the main focus at Google seems to be on objective relevance and quality at the moment, which does not mean that this can change again in the future.

  • Relevance always refers to the document in relation to the search query and its search intent
  • Pertinence relates the document in relation to the individual needs of each user
  • Quality refers to site ranges, domains and entities.

About Olaf Kopp

Olaf Kopp is Co-Founder, Chief Business Development Officer (CBDO) and Head of SEO at Aufgesang GmbH. He is an internationally recognized industry expert in semantic SEO, E-E-A-T, modern search engine technology, content marketing and customer journey management.As an author, Olaf Kopp writes for national and international magazines such as Search Engine Land, t3n, Website Boosting, Hubspot, Sistrix, Oncrawl, Searchmetrics, Upload … . In 2022 he was Top contributor for Search Engine Land. His blog is one of the most famous online marketing blogs in Germany. In addition, Olaf Kopp is a speaker for SEO and content marketing SMX, CMCx, OMT, OMX, Campixx...


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