Google RankBrainアルゴリズム:SEO戦略への影響を解き明かす

Google RankBrain is a machine learning-based search engine algorithm update which has significantly impacted the way search results of search intents are determined since its introduction in 2015. 

This component of Google’s core algorithm uses artificial intelligence to process search queries and provide more relevant results for users. RankBrain’s primary function is to understand and interpret the intent behind users’ search queries, allowing Google to display the most accurate and useful information in response.

Google RankBrainアルゴリズム

As a part of Google’s ever-evolving algorithm, RankBrain has become an essential aspect of search engine optimization (SEO). By continuously learning from user input and adjusting its understanding of サーチクエリ, RankBrain helps the search engine better adapt to new trends, languages, or user behavior. Consequently, businesses and website owners must keep themselves updated with RankBrain’s developments to ensure the optimal visibility of their online content.

Google RankBrainがどのように機能し、検索結果に与える影響を理解することは、競争の激しいデジタル環境で優位に立つために極めて重要です。ランキングプロセスにおけるRankBrainの役割を考慮することで、ウェブサイトのオーナーは効果的な対策を考案することができます。 SEO対策 コンテンツがターゲットとする読者にとって、適切で、有益で、見やすいものであり続けるようにするためだ。

So, how rankbrain works?

Google RankBrainアルゴリズムを理解する

Google RankBrain Algorithm is a significant component of Google’s core search algorithm. It leverages machine learning and artificial intelligence to analyze search queries, improve user intent understanding, and determine the most relevant search results1.このAIシステムは、グーグルの検索結果の正確性と関連性を維持するために重要な役割を果たしている。

RankBrainが導入される以前は、Googleはユーザーに検索結果を表示するために、手作業でコード化されたアルゴリズムだけに頼っていた。2.RankBrainの主な機能は、セマンティクス、コンテキスト、クリックスルー率や滞留時間のようなユーザー満足度の指標など、さまざまな要素を考慮して検索クエリを処理し、理解することである。3. Google’s RankBrain has significantly enhanced the search algorithm by making it more adaptable and efficient.

The integration of machine learning with Google’s existing algorithm allows RankBrain to continuously learn and refine its understanding of how words are related to concepts4.これは、Googleがより関連性の高いサービスを提供できるようになったことを意味する。 コンテンツ RankBrainが他の単語や概念との関連性を識別するため、検索クエリに正確な単語がすべて含まれていなくても、検索される。

Google RankBrainアルゴリズムに関する重要なポイントをいくつか挙げる:

  • 機械学習と人工知能を活用
  • ユーザーの意図の理解を深める
  • 最も関連性の高い検索結果を決定
  • Comes integrated with Google’s core search algorithm
  • データ入力から継続的に学習する

In summary, Google RankBrain Algorithm is a powerful AI system that serves as a crucial element of Google’s core search algorithm. By leveraging machine learning and artificial intelligence, RankBrain has significantly improved the efficiency and accuracy of Google search results.



SEOにおけるGoogle RankBrainの役割

The Google RankBrain algorithm plays a crucial role in Search Engine Optimization (SEO). As a machine learning (AI) algorithm, RankBrain helps Google process understand search queries and sort search results for better relevance. This is especially important given the vast amounts of data handled by search engines every day1.


  • Relevance: RankBrain’s primary function is to determine the most relevant results to search engine queries2.クエリの背後にあるユーザーの意図を理解することで、より正確な結果を提供することができる。
  • Links: Links continue to be an essential ranking factor in SEO. Google values high-quality backlinks as a signal of a website’s relevancy and authority3.
  • User location: RankBrain adapts search results based on a user’s location, ensuring a personalized experience with local search results4.

Here’s a brief overview of how these entities are connected:

事業体Role in Google RankBrain
SEOEnhances a site’s visibility
ユーザーロケーションパーソナライズ サーチ・エクスペリエンス

その人工知能の能力にもかかわらず、RankBrainは数ある人工知能のひとつに過ぎない。 ランキングシグナル Googleが使用する。当然ながら、SEOの専門家は、さらに以下の点にも注意を払わなければならない。 ランキング要因 and adapt their strategies by ensuring their site meets Google’s quality guidelines.

要約すると、Google RankBrainはSEOの重要な要素であり、関連性、リンク、ユーザーの位置などの様々な要素を考慮することで、サイトのランキングに影響を与える。これらの要素の最適化に注力することで、ウェブサイトは可視性を向上させ、検索結果でより良いパフォーマンスを発揮することができる。




Google’s RankBrain is a complex component of the search engine’s ranking algorithm. At its core, RankBrain is a machine learning system that builds on earlier iterations of Google’s algorithm, like Hummingbird1.要するに、文字通りの読解から焦点を移すのである。 キーワード 検索クエリのコンテキストとセマンティクスを理解する。

There are several key search ranking factors that RankBrain considers when analyzing search queries and providing results2. These factors include user experience signals, domain authority, quality content, and backlink profiles. RankBrain constantly refines search results by learning from user behavior and adapting its algorithms accordingly.

One of the main advantages of RankBrain lies in its ability to leverage machine learning. Through machine-learning algorithms, RankBrain can analyze search queries and provide results that match a user’s intent3.単なるキーワードマッチングにとどまらず、同義語や関連語など、クエリのさまざまな側面をインテリジェントに結びつける。

RankBrain’s algorithms work in tandem with other Google components to deliver a comprehensive search experience. Its machine-learning capabilities are enriched with constant data input from billions of searches, providing the search engine with the necessary intelligence to refine and enhance its understanding of queries and user intent4.

In summary, RankBrain is an integral part of Google’s ranking algorithm, employing machine-learning techniques and considering various search ranking factors to deliver contextually relevant search results. Its algorithms are constantly evolving through data inputs and user behavior analysis, making it a critical aspect of the ever-changing SEO landscape.


  1. A Complete Guide to the Google RankBrain Algorithm – Search Engine Journal 
  2. Understanding RankBrain: Google’s AI Algorithm Explained 
  3. Google RankBrain: The Definitive Guide – Backlinko 
  4. Understanding Google Rank Brain And How It Impacts SEO – Moz 


The Google RankBrain Algorithm plays a crucial role in understanding the searcher’s intent and providing relevant results. One essential factor that makes this possible is the analysis of keywords and queries.

When users enter a search query, they typically use strings of words that convey their needs. With this query, Google’s RankBrain attempts to provide the most accurate results. Thus, the importance of keyword research cannot be understated. キーワード調査 コンテンツ制作者やデジタルマーケティング担当者は、人々が検索している単語やフレーズを特定し、検索エンジンのランキングを上げるためにウェブサイトを最適化することができます。

The incorporation of long-tail keywords into this approach makes a significant impact. Longer and more specific phrases tend to cater to a niche audience, making it easier for RankBrain to provide relevant search results. These long-tail keywords often yield better conversion rates, as they target users who are more likely to make a purchase or take a desired action.

It’s important to consider keyword matching when it comes to content marketing, as it directly influences the appearance of search results. Google’s RankBrain Algorithm goes beyond mere keyword matching and emphasizes understanding the context and semantics behind a search query1.


  1. Keywords – The foundation of every search query, they effectively describe what the user is looking for.
  2. Search Query – The combination of keywords that users input into a search engine.
  3. Keyword Research – The process of identifying popular words or phrases people are searching for to better understand market demand and optimize content.
  4. Long-Tail Keywords – Longer and more specific phrases that cater to a particular niche and produce better conversion rates.
  5. Keyword Matching – Aligning the use of keywords in content for improved search engine rankings, considering context and semantics.

In conclusion, understanding the workings of the Google RankBrain Algorithm and its approach to keywords and queries can provide significant improvements concerning search engine rankings and user conversions. Incorporating long-tail keywords, focusing on keyword research, and considering keyword matching will lead to better results in a constantly changing digital landscape.


  1. 検索クエリの背後にあるコンテキストとセマンティクスを理解する 


Google’s RankBrain algorithm has a primary focus on understanding the user intent behind a search query. The concept of user intent, often referred to as searcher intent, revolves around determining the ultimate goal a user has when conducting an online search. By comprehending this intent, Google can deliver more accurate and relevant search results for users.

A key aspect of RankBrain’s functionality is its ability to analyze search queries using machine learning algorithms. These algorithms facilitate a deeper understanding of the context and semantics associated with a query, rather than focusing solely on keyword matching Understanding RankBrain: Google’s AI Algorithm Explained.このレベルの理解を得るために、RankBrainはユーザーの嗜好、検索履歴、クリックスルー率などさまざまな要素を考慮する。


  1. Informational: The goal is to gather information, like researching a topic or seeking an answer to a question. Example: “How does Google RankBrain work?”
  2. Navigational: The searcher wants to find a specific website, webpage, or online resource. Example: “Search Engine Journal RankBrain guide”
  3. Transactional: The objective is to perform an online transaction, like purchasing a product or signing up for a service. Example: “Buy organic SEO optimization services”

By acknowledging the importance of user intent, Google’s RankBrain is able to filter content that is better at fulfilling a searcher’s purpose Google RankBrainアルゴリズム完全ガイド.これにより、アルゴリズムの全体的な有効性が高まり、表示されるコンテンツが使用されたキーワードに関連するだけでなく、検索された文脈にも関連することが保証される。

In conclusion, understanding user intent is an essential aspect of RankBrain’s performance. By focusing on searcher intent, the algorithm can provide results that align more closely with the user’s expectations, offering a better user experience and ensuring that the most appropriate content is surfaced.


Google’s RankBrain algorithm has revolutionized the way it interprets search queries by using Natural Language Processing (NLP). NLP helps the algorithm better understand the intent of a user’s search, taking into account not just individual keywords but also context, semantics, and other language patterns.

RankBrainにおけるNLPの重要な側面の1つは、次のものを使用していることだ。 word vectors.単語ベクトルは、単語やフレーズを多次元空間にマッピングし、アルゴリズムがそれらの間の関係や類似性を識別することを可能にする。そうすることで、RankBrainは同義語や似た意味を持つフレーズなど、言語のニュアンスを理解し、より関連性の高い検索結果を提供することができる。


  • 人工知能
  • AI
  • 機械学習

A traditional keyword-based algorithm might treat them as distinct entities. However, RankBrain’s NLP allows it to recognize their interconnectedness and provide search results that cater to the broader context.

Another critical component of RankBrain’s NLP is its ability to comprehend the コンテキスト in which words and phrases are used. This understanding enables it to discern the meaning behind a search query more accurately, even when the query contains ambiguous or complex language. As a result, Google’s search results become more aligned with the user’s actual intention.

理解する能力 セマンティクス は、NLPとRankBrainの中心でもある。RankBrainは、単語の意味と単語間のつながりを把握することで、以前のアルゴリズムでは困難だった検索クエリを効果的に解析することができる。これは セマンティック また、慣用的な表現やスラングを含むクエリを処理する際にも、より正確な結果を得ることができる。

In summary, RankBrain’s NLP capabilities have enhanced its ability to interpret search queries by considering natural language patterns, context, and semantics. The use of word vectors further solidifies its understanding of relationships between words and phrases, ultimately leading to more relevant search results for users.


Google RankBrain Algorithm prioritizes user experience and behavior while determining search results. This focus encourages website owners to create content that caters to the needs of their audience effectively. The attention RankBrain pays to user behavior encompasses several factors, such as engagement, dwell time, bounce rate, and pogo-sticking.

Engagement plays a crucial role in RankBrain’s evaluation process. Websites that deliver relevant content and encourage user interaction are more likely to be rewarded with higher rankings. This can be achieved through responsive web design, clear calls to action, and content that genuinely resonates with the target audience.

It also assesses the dwell time—the amount of time users spend on a webpage after clicking through from search results. A longer dwell time often indicates that the user has found the content valuable and informative. RankBrain may interpret this as a sign that the page provides a good user experience and therefore should rank higher in search results.

Another factor RankBrain considers is the bounce rate. This refers to how quickly users leave a website after only visiting one page. A high bounce rate may indicate that the website’s content is not meeting the needs of the users or that the layout and navigation are confusing. Websites that 管理する 直帰率を低く保つことは、より良いユーザー体験を提供しているとみなされる可能性が高い。

Pogo-sticking is a negative user behavior that occurs when users quickly navigate back to the search results after visiting multiple pages in rapid succession. This action suggests that the user is not finding what they are looking for, and RankBrain factors this into its evaluation. Minimizing pogo-sticking by providing relevant and well-organized content will improve the likelihood of ranking higher in search results.

まとめると、Google RankBrainアルゴリズムは、ユーザー体験と行動を重要視している。エンゲージメント、滞留時間、直帰率、ポゴスティッキングの制限などの要素を最適化することで、ウェブサイトのオーナーは、より良いウェブサイトを作ることができる。 ユーザー・エクスペリエンスを向上させ 検索エンジンのランキング


The Google RankBrain Algorithm is an artificial intelligence (AI) system that significantly improves the way search queries are interpreted and processed. One of the key aspects of RankBrain is its ability to recognize and analyze entities. Entities, in this context, refer to people, places, things, or concepts that are interconnected within a given query or text. By understanding these entities, RankBrain helps deliver more accurate and relevant search results.

The foundation of RankBrain’s entity recognition lies in its ability to understand and match search queries to key concepts within Google’s ナレッジグラフ. The Knowledge Graph is a large, interlinked database containing vast amounts of data about various concepts, their relationships, and their attributes. In leveraging this Knowledge Graph, RankBrain can determine the context and semantic meaning behind users’ search queries.

ユーザーが検索クエリを入力すると、RankBrainは入力されたキーワードやフレーズをナレッジグラフに存在する関連エンティティと関連付けようとする。このプロセスにより、アルゴリズムは 真意を読み解く 場所、パーソナライズ、使用されている単語などの要素を考慮し、検索者の。そうすることで、より正確で適切な検索結果を提供することができる。

One example of entity recognition in RankBrain is its ability to differentiate between homonyms or words with multiple meanings. Consider the word “apple”: it could refer to the fruit, the technology company, or even a specific product. RankBrain uses its understanding of the query’s context, based on the entities within the Knowledge Graph, to determine which meaning of “apple” is relevant and should be shown in the search results.

In summary, entity recognition is a crucial component of Google’s RankBrain Algorithm. It enables the system to better understand and process search queries by associating them with relevant entities and concepts within its Knowledge Graph. As a result, Google can deliver more accurate and contextually appropriate search results to users.


In the world of search engine optimization (SEO), Google’s RankBrain algorithm plays a crucial role in delivering the most relevant results to users’ queries. Two essential factors RankBrain considers are backlinks and content freshness. These elements contribute significantly to the search engine rankings of websites, ensuring users find up-to-date and authoritative information.

Backlinks are incoming links from external websites that point to a specific web page. They act as a vote of confidence from one site to another, signaling the linked content’s relevance, reliability, and authority. Google’s RankBrain algorithm takes into account the number and quality of backlinks a webpage has, influencing its search results rankings. Websites with higher numbers of quality backlinks are generally considered more trustworthy and authoritative, leading to higher positions in search engine results pages (SERPs) 1.

On the other hand, content freshness refers to the recency and relevance of the information presented on a webpage. Google’s RankBrain algorithm favors newly updated content, as it is more likely to provide accurate and up-to-date information 2.しかし、クエリによっては、コンテンツの鮮度が他のクエリよりも重要な場合がある。例えば、ニュース記事やトレンドトピックは、通常、新鮮なコンテンツを必要としますが エバーグリーン・コンテンツ歴史的事実や一般的な知識のように、一刻を争うものではないかもしれない。

In conclusion, Google’s RankBrain algorithm strives to deliver the most relevant and reliable results to users by considering factors such as backlinks and content freshness among others. Properly managing these factors can enhance a website’s search engine rankings, providing users with the valuable information they seek 3.

Google RankBrainに最適化する方法

Google RankBrain is an essential component of the search engine’s algorithm, and optimizing your website for it can help improve your search rankings. Here are a few steps to consider when tailoring your content for RankBrain.


To optimize your site, focus on creating high-quality and relevant content that answers users’ queries effectively. Be thorough yet concise, and cover important points to ensure the article’s value. Remember, a よく書かれた有益な情報 その結果、エンゲージメント、ユーザー体験、検索順位が向上する。


User experience (UX) signals, such as click-through rate, time spent on a page, and bounce rate, factor into RankBrain’s assessment. To optimize for these, ensure your website is user-friendly with a clean design and easy navigation. Additionally, 高速読み込みページ とモバイルフレンドリーは、UXシグナルを改善し、その結果、検索ランキングを向上させることができる。


Googleは検索クエリにマッチするようにメタディスクリプションを変更することがありますが、キーワードが豊富でよく練られたメタディスクリプションは、検索クエリにマッチします。 メタディスクリプション でもクリック率を向上させることができます。を正確に表現する、説得力があり情報量の多いメタディスクリプションを書くようにしましょう。 コンテンツ あなたのページの

Incorporating these guidelines into your website strategy can help optimize your content for Google RankBrain. With a focus on quality, user experience, and relevance, you can improve your chances of achieving higher search rankings and increasing your website’s visibility.

Google RankBrain対ハミングバード対PageRank

Google has continuously evolved its search algorithms to provide users with the most relevant results and exceptional experience. Among these algorithms are Google RankBrain, Hummingbird, and PageRank. While each has its specific impacts on digital マーケティング と検索結果は、互いに異なる動作をする。ここでは、これらの機能を簡単に説明し、比較する。


2015年に導入された、 ランクブレイン は、機械学習ベースのアルゴリズムで、検索クエリを理解して処理し、関連性の高い結果を返す役割を担っている。前身とは異なり、RankBrainはデータ入力から自己学習することで継続的に進化する。その結果、このアルゴリズムは、特に曖昧なクエリや複雑なクエリに対して、検索結果の精度を高める上で重要な役割を果たしている。


Launched in 2013, the Hummingbird algorithm was a significant update to Google’s search engine, focusing on understanding the intent and context behind an entire search query rather than just individual keywords. It brought about semantic search, enabling Google to provide more accurate results by considering synonyms, themes, and the relationship between words. While RankBrain focuses on learning and understanding queries, Hummingbird ensures the broad context is accounted for, leading to more targeted and high-quality search results.


PageRank is the earliest algorithm developed by Google for ranking web pages in the search engine results pages (SERPs). Named after Larry Page, one of the co-founders of Google, the algorithm primarily assesses the quality and quantity of the external links pointing to a webpage. High-quality backlinks indicate a greater likelihood of the page being valuable and relevant to users, thus improving its position in the SERPs.

In summary, RankBrain, Hummingbird, and PageRank all contribute to the process of producing relevant search results for users. RankBrain leverages machine learning to understand and process search queries, Hummingbird examines the context and intent behind entire queries, and PageRank focuses on the quality and quantity of backlinks. As digital marketing in the United States and elsewhere continues to evolve, Google’s algorithms will undoubtedly keep refining and revolutionizing the search experience.


近年では Google RankBrainアルゴリズム は、検索エンジン最適化(SEO)の世界に大きな影響を与えた。アルゴリズムが改良され更新され続けるにつれ、ユーザーのクエリを理解し、正確で関連性の高い検索結果を提供するためのアプローチがますます洗練されていくことが予想されます。

At its core, RankBrain is a machine learning (AI) algorithm designed to help Google process and understand search queries more effectively. By applying artificial intelligence, RankBrain can interpret the intent behind a user’s search terms, even when dealing with ambiguous phrases or synonyms. This allows the algorithm to provide more 関連検索結果 その結果、ユーザーの満足度が高まる。

With RankBrain becoming increasingly integral to Google’s search algorithm, SEO strategies must adapt accordingly. To remain effective in this evolving landscape, it is essential for SEO efforts to prioritize user-focused content that demonstrates a deep understanding of the ターゲット audience’s needs and interests. By creating high-quality, informative content, websites can improve their chances of being recognized and rewarded by RankBrain and other search engine algorithms.


  • Keyword use: It is vital to continue researching and employing relevant keywords, but also to incorporate synonyms and related phrases to improve content’s comprehensibility for RankBrain.
  • Content depth: Offer detailed and comprehensive content to ensure maximum usefulness to users. This can result in increased user satisfaction and engagement, sending positive signals to RankBrain and other search algorithms.
  • Website structure: Organize website content clearly and logically, making it easily accessible to users. This helps improve user satisfaction, which can, in turn, enhance visibility in search engine rankings.



  • How does RankBrain impact search ranking?
  • What’s the difference between RankBrain and BERT?
  • 機械学習がRankBrainに与える影響とは?
  • RankBrainアルゴリズムにとってユーザー体験はどれほど重要か?
  • RankBrainにコンテンツを最適化するための効果的な戦略とは?
  • RankBrainはこれまでのGoogleアルゴリズムと比較してどうなのか?

Published on: 2023-11-23
Updated on: 2023-12-18



アイザック・アダムス・ハンズは、検索エンジン最適化サービスを提供するSEO North社でSEOディレクターを務めています。SEOのプロフェッショナルとして、アイザックはオンページSEO、オフページSEO、テクニカルSEOの分野で豊富な専門知識を持ち、競合他社を圧倒している。