How Google can personalize search results?

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The personalization of search results is one of the last steps in the ranking process and is applied after relevance scoring and quality classification.

As part of the November 2024 core update, there were speculations from the SEO community that the update was a personalization update. I myself was one of the first SEOs to share this assumption:

But what does this mean and to what extent can Google personalize search results? Based on official Google info, Google patents, the data from the API Leak and the info from the DOJ trial this article provides thoughts and ideas on this question.The Patent and Paper Analyzer, SEO Thought Leader Research Assistant and Google Antitrust trial & API Leak Analyzer supported me in this research.

How personalization at Google works?

According to official documentation personalization at Google is primarily limited to factors such as the user’s location, language, time, platform used, and the distribution to different data centers. Other factors like click-through rate, past searches, and other user signals have neither been confirmed nor denied by Google. However, personalization is about a user-related individual ranking and not about a general scoring of certain content.

For search queries that have a reference to entities, the search history, i.e., past search queries made with reference to the respective entity, plays a role. For example, if you search for an entity with an ambiguous name like “apple” the search results you get will be influenced by your past searches related to “apple”

Google emphasizes that search results are only minimally personalized based on interests and search history. The most common form of personalization takes place based on the searcher’s location and set language. The results displayed in the news carousel are never personalized.

You can update or change the personalization settings or preferences in your Google account at any time. You can also find content preferences like SafeSearch in settings, which help you make a choice about whether search results include graphic content that may be shocking for some users.

From the DOJ Antitrust trial documents the following conclusions can be drawn:

Location-Based Personalization:

Google personalizes results based on location for both desktop and mobile searches
As confirmed by Google’s representative: “Location matters on desktop also. If I come and search for weather on desktop, I hope I would get weather in Washington, D.C. as against, you know, in Chicago.”

User Data Storage:

Google maintains user search data for extended periods
The data is anonymized and de-identified
When asked about how long they keep data about searches (like “Taylor Swift”), the Google representative indicated they may keep anonymized data indefinitely, though they weren’t the expert on log storage specifics

Machine Learning and User Data:

Google uses user data for training their models
They might train models on specific subsets of users (e.g., U.S. users for U.S.-specific models)
For global models, they look at all users’ data
The volume of data increases over time – a month of data today contains more clicks and queries than the same period three years ago

It is important to distinguish between user data that is used for general relevance and quality assessment and user data that is used for user-specific personalization. Ranking systems based on user data, such as Navboost or Deeprank, are used for general evaluation, not for personalization.

Google’s personalization method, as described in the patent “Personalizing search results” (Patent ID: US8977630B1), involves re-ranking search results based on user preferences. This is achieved by modifying the global ranking algorithm to reflect individual or group biases. The aim is to enhance search result relevance by integrating user-specific preferences into the ranking process.

The patent explains that existing search engines use global ranking algorithms to rank documents based on search queries, but these algorithms do not account for individual user preferences, which can lead to less relevant search results for different users.

The patent claims involve identifying a first set of documents associated with a user, with each document being assigned a weight reflecting the user’s interest.

You can find more details about this patent here.

Which metrics Google is using for personalization?

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About Olaf Kopp

Olaf Kopp is an online marketing expert for Generative Engine Optimization (GEO) and SEO. He has over 15 years of experience in Google Ads, SEO, and content marketing. Olaf Kopp is one of the early pioneers in the fields of Generative Engine Optimization (GEO) and digital brand building, and the inventor of modern GEO and marketing concepts such as LLM readability, brand context optimization, and digital authority management. Olaf Kopp is Co-Founder, Chief Business Development Officer (CBDO) and Head of SEO & AI Search (GEO) at Aufgesang GmbH. He is an internationally recognized industry expert in semantic SEO, E-E-A-T, LLMO & Generative Engine Optimization (GEO), AI- and modern search engine technology, content marketing and customer journey management. Olaf Kopp is one of the first pioneers worldwide to have demonstrably worked on the topics of Generative Engine Optimization (GEO) and Large Language Model Optimization (LLMO). His first publications date back to 2023. 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, SERP Conf., CMCx, OMT, OMX, Campixx...