Matching search queries and document features
Posted: Wed Feb 19, 2025 6:41 am
Objective, query-independent relevance assessment: Documents are scored based on how well their features match the features of the query. This assessment may include complex algorithms that take into account the frequency and position of keywords in the document, the overall thematic relevance of the document, and the quality of the content.
Query-dependent ranking factors: In addition to basic matching, the search engine also takes into account query-dependent factors such as the user's location, the device used, personalization settings or search history, which can influence the relevance of documents for the respective query.
use of previous interactions
Learning from user behavior: Using actions associated with document south korea cell phone number list and query characteristics based on past interactions. This means that the search engine learns how users have interacted with documents in the past (click rates, time spent on a page, etc.) to predict which documents might be more relevant or useful for a given query.
Ranking adjustment: The relevance scores and ranking of documents can be adjusted based on learned behavior, favoring documents that have answered similar search queries in the past.
Ranking search result documents, US10970293B2
The user signals that Google receives are used to train the ranking algorithms, specifically the machine learning algorithms that reorder the results. You can read how this works in my summary of the Training a Ranking Model patent .
Sitewide website area and domain level
At the domain level, website areas or entire domains are classified in terms of their quality according to experience and expertise. The Help Content System, https, Page Experience... can play a role here.
As described in the Website Representation Vectors patent, these websites can then be classified into different levels of competency related to specific topics.
Here, too, deep learning models can be used to identify patterns of high-quality authors and topic authority.
Query-dependent ranking factors: In addition to basic matching, the search engine also takes into account query-dependent factors such as the user's location, the device used, personalization settings or search history, which can influence the relevance of documents for the respective query.
use of previous interactions
Learning from user behavior: Using actions associated with document south korea cell phone number list and query characteristics based on past interactions. This means that the search engine learns how users have interacted with documents in the past (click rates, time spent on a page, etc.) to predict which documents might be more relevant or useful for a given query.
Ranking adjustment: The relevance scores and ranking of documents can be adjusted based on learned behavior, favoring documents that have answered similar search queries in the past.
Ranking search result documents, US10970293B2
The user signals that Google receives are used to train the ranking algorithms, specifically the machine learning algorithms that reorder the results. You can read how this works in my summary of the Training a Ranking Model patent .
Sitewide website area and domain level
At the domain level, website areas or entire domains are classified in terms of their quality according to experience and expertise. The Help Content System, https, Page Experience... can play a role here.
As described in the Website Representation Vectors patent, these websites can then be classified into different levels of competency related to specific topics.
Here, too, deep learning models can be used to identify patterns of high-quality authors and topic authority.