🔄 What is Re-ranking for SEO?

Re-ranking is the process of evaluating and modifying the initial rankings and existing rankings of a query result instance. A Search Engine Result Page includes the filtered web page documents for a specific query based on relevance, quality, reliability, authority, popularity, and originality. The re-ranking algorithm’s purpose is to support the initial rankings by increasing the efficiency, quality, and sustainability of the SERP with different SERP Features, and sources on the web. 🚀

Re-ranking algorithms for a search engine are crucial to continuing to satisfy the users’ possible and related search activities. Calculating the different search intent possibilities based on simple or complex user queries, and matching these search intents with different documents, requires different types of re-ranking algorithms. A Re-ranking process can be evaluated based on relevance, and another one can be evaluated based on the freshness of the document. 💡

🤔 How Does Re-ranking Work in SEO?

When a web page is initially ranked, the re-ranking process starts after the first feedback from the web. The feedback from the web after the initial ranking process can be a timespan ⏳, user behavior 👥, source changes 🔄, web page changes 📝, external reference changes 🔗 along with internal and external popularity signals. Re-ranking algorithms can be triggered to make a Search Engine reevaluate a web page so that the rankings can be changed.

To trigger a re-ranking process, the web page document can have a new external reference, internal reference, or a content change. A re-ranking trigger can be acquired with controllable and uncontrollable factors for SEOs.

Uncontrollable factors of re-ranking triggers for SEO are trending news 🗞️, query demand changes 📈, Search Engine Algorithm Updates ⚙️, or bugs 🐞, and seasonal changes ❄️☀️.

Controllable factors of re-ranking triggers for SEO are external, and internal changes at the level of a website 🌐, web page, and intersection of external, internal, website, and web page areas. Such social media accounts of the brand are internal for the brand but external for the website in the context of SEO evaluation. 📲

What are the Re-ranking Methods and Criteria of Search Engines? 😎

The criteria and methods that can be used by a search engine for reranking the web page documents for user queries are listed below.

1. 🔄 Rerank Search Results by Filtering Duplicate, or Near-Duplicate Content

A search engine can filter the duplicate content or highly similar content to the other instances without any additional value or prominent source attribute. Even if a copy or duplicate content is initially ranked, the specific content can be negatively re-ranked by the search engine based on other criteria.

2. 🚫 Rerank Search Results by Removing Multiple Relevant Pages from the Same Site

There are three different possible outcomes of multiple relevant pages for a specific query:

• Cannibalization: If two web pages are closely related to a query, they can cannibalize each other and create ranking signal dilution.
• Clustering for Different Subqueries: If two web pages are competing with each other for different attributes of the same entity by subqueries, they can support each other by being clustered.
• Outranking Computation: The more relevant, externally, and internally popular page can outrank the second page to be re-ranked.

3. 👤 Rerank Search Results Based Upon Personal Interests

Personalization of the user and user segments is closely related to the historical data of a web page and its source. A search engine can:

• Track a user’s interests.
• Cluster the user with a user segment.
• Modify the search results for better quality SERP instances.

A query pattern, past search activity, and visited sites can affect the rankings on the SERP for individual users. The modified ranking results for personal interests can be used to adjust the default SERP versions for specific trends or instances without any personal information.

4. 🌐 Rerank Search Results Based Upon Local Interconnectivity

Local interconnectivity between web page documents includes the links between documents within a closely related web graph. For example:

• If there are “n number of documents” for a query and these documents mostly link to certain sources,
• And if these referenced sources link to some other sources,
• Then, local interconnectivity for specific documents can be measured.

(For instance, Google’s Programmable Search Engine includes a parameter for retrieving documents that only link a specific document for a query. To learn how to use the Custom Search Engine API of Google with Python, read the related guideline.)

5. 🌍 Rerank Search Results by Sorting for Country-Specific Results

A user may want to see results only from a country-specific domain or country-specific IP. Considerations include:

• A topic or entity can be more popular in a specific country.
• The IP Address of the source (website) can affect local search rankings. For example, if a domain has an IP Address from Poland or Australia, it can have higher rankings for users from that country.
• Even if the IP Address is from another country, if the source has many external link references from other countries, the search engine can relate the source to those localities.
• If the source has extensive content about a country, it can be related to that country during reranking processes.

6. 🗣️ Rerank Search Results by Sorting for Language-Specific Results

A user’s operating system language, browser language, or communication language can affect the reranking process for specific queries. Points to note:

• Query language and preferred document languages can affect the reranking computation.
• In multilingual countries, the results can be mixed with both country-specific and language-specific results based on the query language.
• Authority for a topic and the initial-ranking potential of a source can help a website rank higher across different languages, regions, devices, browsers, operating systems, and user languages.

7. 👥 Rerank Search Results by Looking at the Population or Audience Segmentation Information

Audience Segmentation involves creating a demographic profile that includes:

• Gender, age, location, interest, income, occupation, character, and condition profile for larger user segments.

The process:

• The first population assignment is created with the initial ranking score during document retrieval.
• When the document is retrieved, it is matched with an audience (the first population), generating a “selection score.”
• The same process occurs for a second document.
• Then, the selection scores and audiences are compared to create a better-consolidated population and audience segmentation.
• Once finalized, the selection scores for queries and documents are refreshed, completing the reranking process based on audience segmentation.
• Historical data is used during this audience segmentation process.

8. ⏳ Rerank Search Results Based Upon Historical Data

Historical data can be used to gather information across historical changes for a document. It can define a document based on:

• Changes in content, visual design, layout, brand entity, and external/internal popularity.
• A search-demand change or trending topic can alter the historical importance and quality data of a document.

When historical data is used:

• An older document with a larger dataset for successful click satisfaction feedback can rank higher.
• Historical data deficiency is a disadvantage for the initial-ranking score of new sources for a topic or the entire web.

Systems and methods for modifying search results based on a user’s history

9. 📚 Rerank Search Results Based Upon Topic Familiarity

Topic Familiarity refers to the expertise and detail level of the document—not merely the topical familiarity between different topics. A document may:

• Provide more detailed information on a topic, while another might offer just a basic summary.

A search engine can:

• Understand the user’s preference and rerank the documents on the SERP accordingly.
• Consider factors such as writing style, opinions, design, source type, stop word count, and information count to gauge a document’s familiarity with the topic.

10. 💼 Rerank Search Results by Changing Orders Based Upon Commercial Intent

According to the search intent, a search engine can change the order of documents on the SERP. For example:

• If a user explicitly indicates that the search intent has commercial characteristics, the search engine can change the SERP design, features, and preferred sources.
• One early example of this was Yahoo’s Mindset.

11. 📱 Reranking and Removing Results Based Upon Mobile Device Friendliness

A search engine can use a Mobile-friendliness Indicator with mobile-friendliness signals to re-rank URLs based on user agents. Key points:

• Usability of the web page is necessary to satisfy the underlying query need.
• For certain user agents, the search engine can re-rank an entire website, groups of webpages, or individual pages.
• Implicit feedback from web search engine users can influence this process, allowing an initial higher ranking that may later be adjusted for mobile devices (phones, tablets, etc.).

12. ♿ Rerank Search Results Based Upon Accessibility

Web accessibility aims to improve the usability of websites for people with disabilities. This is important for SEO because:

• A significant portion of the intended audience may have disabilities.
• Making a website accessible is an advantage to satisfy a broader audience.

Google provides resources like:

• “Voluntary Product Accessibility Templates”
• Lighthouse, Google Developer Guidelines, and PageSpeed Insights API

A search engine can re-rank sources based on their accessibility and friendliness for people with disabilities. For instance, if a website has color contrast issues or visual/non-visual communication problems, its usability and click satisfaction score may decrease. (Sundar Pichai, CEO of Google, has often emphasized “They build for everyone”.)

13. 📰 Rerank Search Results Based Upon Editorial Content

A search engine can understand the theme of a query, and based on that theme, it can favor editorial opinions or content. Thus:

• Having the correct content format and tonality for a group of queries is important.
• A query’s theme—reflected in verbs, nouns, and entities—can lead to certain opinions or sources being favored (or not favored) during reranking.

14. 🔍 Reranking Based Upon Additional Terms (Boosting) and Comparing Text Similarity

A search engine can re-rank results based on text similarity by checking whether the found documents are related to the specific query or group of queries. For example:

• Research and patents like Query-Free News Search show that Google can generate queries, match them to portions of news articles, and compare similarities to filter out irrelevant content.
• Text similarity can help a document rank better during the reranking process.
• Being unique, comprehensive, and more informative can help a source be seen as non-duplicate and authoritative.
• Algorithms such as A5-HIST and A4-COMP are used to generate queries from text segments, while A7-IDF and A6-3 help shorten queries for better results.

15. 🖱️ Reordering Based Upon Implicit Feedback from User Activities and Click-Throughs

Google and other search engines can use implicit user feedback from the SERP and the web page documents to re-rank sources. Consider:

• Patents like “Modifying search result ranking based on implicit user feedback and model of presentation bias” and research such as “Query Chains: Learning to Rank from Implicit Feedback” indicate that longer timeline feedback decreases noise and increases efficiency.
• A search engine can recognize typos (e.g., “Lexis Nexis” vs. “Lexis Nexus”) and may use machine learning to evaluate feedback.
• This method reduces the cost of reranking by focusing on user behavior rather than solely on document content.

16. 👍 Reranking Based Upon Community Endorsement

Community endorsement (e.g., social media shares, collaborative web search behaviors) can signal the popularity, reliability, and relevance of a source or its web pages. This process involves:

• Tracking clicks, selections, session IDs, and the number of times a page is endorsed, bookmarked, shared, mentioned, or quoted.
• Evaluating the naturality and consistency of external link-related references.
• Triggering synonym and query expansion algorithms when appropriate.
• Ultimately, community endorsement can affect the overall SERP and trigger further reranking for related queries.

17. 📊 Reranking Based Upon Information Redundancy

This method uses word distribution probability and answer redundancy to re-rank query results. When the query breadth is narrow or vague:

• The search engine analyzes word distribution probabilities to infer related search intents.
• The goal is to reduce off-topic and repetitive documents on the SERP.
• Given that search engines typically rank 8–10 (or up to 14–20 with extra SERP features) organic results, reranking based on information redundancy increases the overall diversity and satisfaction of the information presented.
• Information redundancy is determined by calculating the word distribution probability for each document and comparing result sets to each other.

18. 📖 Reranking Based Upon Storylines

A search engine can generate storylines from the SERP results:

• A storyline can summarize a single result web page or multiple pages.
• It is used to define the documents thematically.
• Similar documents generate different storylines and are grouped together.

The purpose is to improve SERP quality, diversify result counts by reducing reliance on PageRank alone, and use “focused vocabulary” and co-occurrence possibilities to understand document context. Although not implemented strictly as “storylines,” search engines may also use relevance and fact redundancy to extract facts and prepositions, computing an importance score for each document.

19. 🦠 Reranking by Looking at Blogs, News, and Web Pages as an Infectious Disease

This method involves turning SERP documents into storylines via vocabularies and co-occurrence probabilities. It recognizes that new terms and concepts can begin to co-occur in specific document types and sources over time.

These co-occurrence changes can help detect newsworthy queries and trending terms, allowing the search engine to group sources into storylines or individually re-rank them based on perceived newsworthiness.

20. ⏰ Reranking Based Upon Conceptually Related Information Including Time-Based and Use-Based Factors

A search engine can group documents based on co-occurring terms and re-rank by regrouping them based on user affinity or the user segment. Additional factors include:

• User interaction and location.
• Changes in user selection that may alter co-occurring term groupings.
• The search engine can refresh its grouping choice while reranking the SERP documents to accommodate time-based and usage-based factors.

21. ⏰ Reranking Based Upon Conceptually Related Information

A search engine can group the documents based on co-occurring terms. It can then re-rank by regrouping them based on user affinity or the user segment. As user interactions, location, and selection change, the grouping is refreshed if the co-occurring terms change, ensuring that the SERP remains dynamically updated.

22. 🔀 Blended and Universal Search

Blended Search, also known as Universal Search, mixes various types of search results (images, podcasts, movies, videos, news, dictionaries, questions, answers, knowledge panels, etc.) into the regular blue link results. This approach can alter the order of the results, sometimes pushing some onto a second page. Additionally, documents more relevant for image or other vertical searches may be boosted based on user feedback.

23. 📝 Phrase-Based Indexing

A search engine differentiates between good and bad phrases. Unknown entities, topics, or queries can be detected if they form a “good phrase” sample. This helps expand the knowledge base and fact repository while keeping it clean and efficient.

By grouping phrases and checking their co-occurrence frequency, the engine can re-rank SERP results. This method is crucial for spam detection, phrase taxonomy creation, and understanding query breadth. Good phrases from top-ranking results, authoritative sources, and side contexts improve overall relevance.

24. 📊 Time-Based Data and Query Log Statistics

A search engine can modify the universally created SERP instance by re-ranking documents based on time-sensitive factors. Since query meaning and intent can change from morning to night or across seasons, the engine adapts its ranking methodology to better capture these variations.

25. 🧭 Navigational Queries

Navigational queries are intended to direct users to a specific webpage. Indicators such as click count, click reversion, mouse-over actions, and result selection time help determine if a query is navigational.

If a user clicks only one result and the document prominently features a brand, location, or name entity, it may be classified as navigational. The detection system can re-rank both navigational and non-navigational documents, boosting similar sources to enhance historical data with targeted user segments.

26. 🔍 Patterns in Click and Query Logs

Query logs contain the search terms and the documents retrieved, while click logs record click events. When users perform sequential queries—such as a path like "banana → apple → berries"—these patterns help the search engine cluster queries chronologically.

By analyzing these sequential queries, the engine identifies search behavior patterns, determines “content terminuses,” and adjusts relevance scores based on the overall search session context.

27. 🤝 TrustRank

TrustRank evaluates trust signals for a source or document based on links and user feedback. Originally coined by Yahoo and later adopted by Google, it considers factors such as annotations, labels, and the authority of linking pages.

While Google's approach incorporates user feedback (e.g., via Sidewiki, Local Experts), Yahoo's method is more link-based. TrustRank helps re-rank documents by emphasizing the trustworthiness of the source.

28. 💬 Social and Community Evidence for Quality

Social and community evidence indicates a webpage’s prominence within a specific country or industry. Shares, reviews, mentions, and other social signals can influence both ranking and re-ranking, ensuring that content with high community approval gains prominence.

29. 🔄 Customization Based Upon Previous Related Queries

This method tailors SERP results based on sequential query logs. If two queries are related, subsequent queries can adjust the search results specifically for that session. Regional or language signals can further customize the ranking to favor sources more relevant to a specific area.

Additional factors such as misspellings, correlated user behavior, and lexical relations (synonyms, antonyms, acronyms) also play a role.

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30. 🔗 Being Linked to by Blogs

Certain links are weighted more heavily based on their source. Links from blogs, patents, and other methodologies (as seen in Microsoft’s approach) may pass more PageRank if they offer real value.

Historically, during the era of Black Hat SEO, tiered blog networks and link farms were common. Over time, the value of such links has diminished unless they provide genuine content. This method differentiates links by type and relevance, combating spammy endorsements.

31. 🏷️ By Ages of Linking Domains

Domain age, while controversial, can influence re-ranking. A mature domain—determined by registration date, first link or crawl time—may boost PageRank, whereas temporary or inconsistent links may be downgraded.

Although Google maintains that domain age is not a direct ranking factor, consistency over time contributes to historical data, selection scores, and trust signals. Microsoft's “Ranking Domains Using Domain Maturity” emphasizes both maturity and the domain's contribution.

32. 🌈 Diversification of Search Results

Diversification is based on Information Foraging Theory and user behavior on the SERP. By displaying various types of documents and SERP features, search engines can cater to different search intents.

For example, search vertical icons (image, video, news, shopping, flight, books) may be reordered based on dominant intent and engagement. This approach ensures that diverse content formats address all possible search intents.

Additional Note: Various methods for ranking and re-ranking—including the initial ranking score definition—work together to generate and update relevance scores based on user interactions.