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Information Retrieval: An Introduction For SEOs

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Information Retrieval: An Introduction For SEOs

When we talk about information retrieval, as SEO pros, we tend to focus heavily on the information collection stage – the crawling.

During this phase, a search engine would discover and crawl URLs that it has access to (the volume and breadth depending on other factors we colloquially refer to as a crawl budget).

The crawl phase isn’t something we’re going to focus on in this article, nor am I going to go in-depth on how indexing works.

If you want to read more on crawl and indexing, you can do so here.

In this article, I will cover some of the basics of information retrieval, which, when understood, could help you better optimize web pages for ranking performance.

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It can also help you better analyze algorithm changes and search engine results page (SERP) updates.

To understand and appreciate how modern-day search engines process practical information retrieval, we need to understand the history of information retrieval on the internet – particularly how it relates to search engine processes.

Regarding digital information retrieval and the foundation technologies adopted by search engines, we can go back to the 1960s and Cornell University, where Gerard Salton led a team that developed the SMART Information Retrieval System.

Salton is credited with developing and using vector space modeling for information retrieval.

Vector Space Models

Vector space models are accepted in the data science community as a key mechanism in how search engines “search” and platforms such as Amazon provide recommendations.

This method allows a processor, such as Google, to compare different documents with queries when queries are represented as vectors.

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Google has referred to this in its documents as vector similarity search, or “nearest neighbor search,” defined by Donald Knuth in 1973.

In a traditional keyword search, the processor would use keywords, tags, labels, etc., within the database to find relevant content.

This is quite limited, as it narrows the search field within the database because the answer is a binary yes or no. This method can also be limited when processing synonyms and related entities.

The closer the two entities are in terms of proximity, the less space between the vectors, and the higher in similarity/accuracy they are deemed to be.

To combat this and provide results for queries with multiple common interpretations, Google uses vector similarity to tie various meanings, synonyms, and entities together.

A good example of this is when you Google my name.

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To Google, [dan taylor] can be:

  • I, the SEO person.
  • A British sports journalist.
  • A local news reporter.
  • Lt Dan Taylor from Forrest Gump.
  • A photographer.
  • A model-maker.

Using traditional keyword search with binary yes/no criteria, you wouldn’t get this spread of results on page one.

With vector search, the processor can produce a search results page based on similarity and relationships between different entities and vectors within the database.

You can read the company’s blog here to learn more about how Google uses this across multiple products.

Similarity Matching

When comparing documents in this way, search engines likely use a combination of Query Term Weighting (QTW) and the Similarity Coefficient.

QTW applies a weighting to specific terms in the query, which is then used to calculate a similarity coefficient using the vector space model and calculated using the cosine coefficient.

The cosine similarity measures the similarity between two vectors and, in text analysis, is used to measure document similarity.

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This is a likely mechanism in how search engines determine duplicate content and value propositions across a website.

Cosine is measured between -1 and 1.

Traditionally on a cosine similarity graph, it will be measured between 0 and 1, with 0 being maximum dissimilarity, or orthogonal, and 1 being maximum similarity.

The Role Of An Index

In SEO, we talk a lot about the index, indexing, and indexing problems – but we don’t actively talk about the role of the index in search engines.

The purpose of an index is to store information, which Google does through tiered indexing systems and shards, to act as a data reservoir.

That’s because it’s unrealistic, unprofitable, and a poor end-user experience to remotely access (crawl) webpages, parse their content, score it, and then present a SERP in real time.

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Typically, a modern search engine index wouldn’t contain a complete copy of each document but is more of a database of key points and data that has been tokenized. The document itself will then live in a different cache.

While we don’t know exactly the processes which search engines such as Google will go through as part of their information retrieval system, they will likely have stages of:

  • Structural analysis – Text format and structure, lists, tables, images, etc.
  • Stemming – Reducing variations of a word to its root. For example, “searched” and “searching” would be reduced to “search.”
  • Lexical analysis – Conversion of the document into a list of words and then parsing to identify important factors such as dates, authors, and term frequency. To note, this is not the same as TF*IDF.

We’d also expect during this phase, other considerations and data points are taken into account, such as backlinks, source type, whether or not the document meets the quality threshold, internal linking, main content/supporting content, etc.

Accuracy & Post-Retrieval

In 2016, Paul Haahr gave great insight into how Google measures the “success” of its process and also how it applies post-retrieval adjustments.

You can watch his presentation here.

In most information retrieval systems, there are two primary measures of how successful the system is in returning a good results set.

These are precision and recall.

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Precision

The number of documents returned that are relevant versus the total number of documents returned.

Many websites have seen drops in the total number of keywords they rank for over recent months (such as weird, edge keywords they probably had no right in ranking for). We can speculate that search engines are refining the information retrieval system for greater precision.

Recall

The number of relevant documents versus the total number of relevant documents returned.

Search engines gear more towards precision over recall, as precision leads to better search results pages and greater user satisfaction. It is also less system-intensive in returning more documents and processing more data than required.

Conclusion

The practice of information retrieval can be complex due to the different formulas and mechanisms used.

For example:

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As we don’t fully know or understand how this process works in search engines, we should focus more on the basics and guidelines provided versus trying to game metrics like TF*IDF that may or may not be used (and vary in how they weigh in the overall outcome).

More resources: 


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Google Further Postpones Third-Party Cookie Deprecation In Chrome

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Close-up of a document with a grid and a red stamp that reads "delayed" over the word "status" due to Chrome's deprecation of third-party cookies.

Google has again delayed its plan to phase out third-party cookies in the Chrome web browser. The latest postponement comes after ongoing challenges in reconciling feedback from industry stakeholders and regulators.

The announcement was made in Google and the UK’s Competition and Markets Authority (CMA) joint quarterly report on the Privacy Sandbox initiative, scheduled for release on April 26.

Chrome’s Third-Party Cookie Phaseout Pushed To 2025

Google states it “will not complete third-party cookie deprecation during the second half of Q4” this year as planned.

Instead, the tech giant aims to begin deprecating third-party cookies in Chrome “starting early next year,” assuming an agreement can be reached with the CMA and the UK’s Information Commissioner’s Office (ICO).

The statement reads:

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“We recognize that there are ongoing challenges related to reconciling divergent feedback from the industry, regulators and developers, and will continue to engage closely with the entire ecosystem. It’s also critical that the CMA has sufficient time to review all evidence, including results from industry tests, which the CMA has asked market participants to provide by the end of June.”

Continued Engagement With Regulators

Google reiterated its commitment to “engaging closely with the CMA and ICO” throughout the process and hopes to conclude discussions this year.

This marks the third delay to Google’s plan to deprecate third-party cookies, initially aiming for a Q3 2023 phaseout before pushing it back to late 2024.

The postponements reflect the challenges in transitioning away from cross-site user tracking while balancing privacy and advertiser interests.

Transition Period & Impact

In January, Chrome began restricting third-party cookie access for 1% of users globally. This percentage was expected to gradually increase until 100% of users were covered by Q3 2024.

However, the latest delay gives websites and services more time to migrate away from third-party cookie dependencies through Google’s limited “deprecation trials” program.

The trials offer temporary cookie access extensions until December 27, 2024, for non-advertising use cases that can demonstrate direct user impact and functional breakage.

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While easing the transition, the trials have strict eligibility rules. Advertising-related services are ineligible, and origins matching known ad-related domains are rejected.

Google states the program aims to address functional issues rather than relieve general data collection inconveniences.

Publisher & Advertiser Implications

The repeated delays highlight the potential disruption for digital publishers and advertisers relying on third-party cookie tracking.

Industry groups have raised concerns that restricting cross-site tracking could push websites toward more opaque privacy-invasive practices.

However, privacy advocates view the phaseout as crucial in preventing covert user profiling across the web.

With the latest postponement, all parties have more time to prepare for the eventual loss of third-party cookies and adopt Google’s proposed Privacy Sandbox APIs as replacements.

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Featured Image: Novikov Aleksey/Shutterstock

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How To Write ChatGPT Prompts To Get The Best Results

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How To Write ChatGPT Prompts To Get The Best Results

ChatGPT is a game changer in the field of SEO. This powerful language model can generate human-like content, making it an invaluable tool for SEO professionals.

However, the prompts you provide largely determine the quality of the output.

To unlock the full potential of ChatGPT and create content that resonates with your audience and search engines, writing effective prompts is crucial.

In this comprehensive guide, we’ll explore the art of writing prompts for ChatGPT, covering everything from basic techniques to advanced strategies for layering prompts and generating high-quality, SEO-friendly content.

Writing Prompts For ChatGPT

What Is A ChatGPT Prompt?

A ChatGPT prompt is an instruction or discussion topic a user provides for the ChatGPT AI model to respond to.

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The prompt can be a question, statement, or any other stimulus to spark creativity, reflection, or engagement.

Users can use the prompt to generate ideas, share their thoughts, or start a conversation.

ChatGPT prompts are designed to be open-ended and can be customized based on the user’s preferences and interests.

How To Write Prompts For ChatGPT

Start by giving ChatGPT a writing prompt, such as, “Write a short story about a person who discovers they have a superpower.”

ChatGPT will then generate a response based on your prompt. Depending on the prompt’s complexity and the level of detail you requested, the answer may be a few sentences or several paragraphs long.

Use the ChatGPT-generated response as a starting point for your writing. You can take the ideas and concepts presented in the answer and expand upon them, adding your own unique spin to the story.

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If you want to generate additional ideas, try asking ChatGPT follow-up questions related to your original prompt.

For example, you could ask, “What challenges might the person face in exploring their newfound superpower?” Or, “How might the person’s relationships with others be affected by their superpower?”

Remember that ChatGPT’s answers are generated by artificial intelligence and may not always be perfect or exactly what you want.

However, they can still be a great source of inspiration and help you start writing.

Must-Have GPTs Assistant

I recommend installing the WebBrowser Assistant created by the OpenAI Team. This tool allows you to add relevant Bing results to your ChatGPT prompts.

This assistant adds the first web results to your ChatGPT prompts for more accurate and up-to-date conversations.

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It is very easy to install in only two clicks. (Click on Start Chat.)

Screenshot from ChatGPT, April 2024

For example, if I ask, “Who is Vincent Terrasi?,” ChatGPT has no answer.

With WebBrower Assistant, the assistant creates a new prompt with the first Bing results, and now ChatGPT knows who Vincent Terrasi is.

Enabling reverse prompt engineeringScreenshot from ChatGPT, March 2023

You can test other GPT assistants available in the GPTs search engine if you want to use Google results.

Master Reverse Prompt Engineering

ChatGPT can be an excellent tool for reverse engineering prompts because it generates natural and engaging responses to any given input.

By analyzing the prompts generated by ChatGPT, it is possible to gain insight into the model’s underlying thought processes and decision-making strategies.

One key benefit of using ChatGPT to reverse engineer prompts is that the model is highly transparent in its decision-making.

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This means that the reasoning and logic behind each response can be traced, making it easier to understand how the model arrives at its conclusions.

Once you’ve done this a few times for different types of content, you’ll gain insight into crafting more effective prompts.

Prepare Your ChatGPT For Generating Prompts

First, activate the reverse prompt engineering.

  • Type the following prompt: “Enable Reverse Prompt Engineering? By Reverse Prompt Engineering I mean creating a prompt from a given text.”
Enabling reverse prompt engineeringScreenshot from ChatGPT, March 2023

ChatGPT is now ready to generate your prompt. You can test the product description in a new chatbot session and evaluate the generated prompt.

  • Type: “Create a very technical reverse prompt engineering template for a product description about iPhone 11.”
Reverse Prompt engineering via WebChatGPTScreenshot from ChatGPT, March 2023

The result is amazing. You can test with a full text that you want to reproduce. Here is an example of a prompt for selling a Kindle on Amazon.

  • Type: “Reverse Prompt engineer the following {product), capture the writing style and the length of the text :
    product =”
Reverse prompt engineering: Amazon productScreenshot from ChatGPT, March 2023

I tested it on an SEJ blog post. Enjoy the analysis – it is excellent.

  • Type: “Reverse Prompt engineer the following {text}, capture the tone and writing style of the {text} to include in the prompt :
    text = all text coming from https://www.searchenginejournal.com/google-bard-training-data/478941/”
Reverse prompt engineering an SEJ blog postScreenshot from ChatGPT, March 2023

But be careful not to use ChatGPT to generate your texts. It is just a personal assistant.

Go Deeper

Prompts and examples for SEO:

  • Keyword research and content ideas prompt: “Provide a list of 20 long-tail keyword ideas related to ‘local SEO strategies’ along with brief content topic descriptions for each keyword.”
  • Optimizing content for featured snippets prompt: “Write a 40-50 word paragraph optimized for the query ‘what is the featured snippet in Google search’ that could potentially earn the featured snippet.”
  • Creating meta descriptions prompt: “Draft a compelling meta description for the following blog post title: ’10 Technical SEO Factors You Can’t Ignore in 2024′.”

Important Considerations:

  • Always Fact-Check: While ChatGPT can be a helpful tool, it’s crucial to remember that it may generate inaccurate or fabricated information. Always verify any facts, statistics, or quotes generated by ChatGPT before incorporating them into your content.
  • Maintain Control and Creativity: Use ChatGPT as a tool to assist your writing, not replace it. Don’t rely on it to do your thinking or create content from scratch. Your unique perspective and creativity are essential for producing high-quality, engaging content.
  • Iteration is Key: Refine and revise the outputs generated by ChatGPT to ensure they align with your voice, style, and intended message.

Additional Prompts for Rewording and SEO:
– Rewrite this sentence to be more concise and impactful.
– Suggest alternative phrasing for this section to improve clarity.
– Identify opportunities to incorporate relevant internal and external links.
– Analyze the keyword density and suggest improvements for better SEO.

Remember, while ChatGPT can be a valuable tool, it’s essential to use it responsibly and maintain control over your content creation process.

Experiment And Refine Your Prompting Techniques

Writing effective prompts for ChatGPT is an essential skill for any SEO professional who wants to harness the power of AI-generated content.

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Hopefully, the insights and examples shared in this article can inspire you and help guide you to crafting stronger prompts that yield high-quality content.

Remember to experiment with layering prompts, iterating on the output, and continually refining your prompting techniques.

This will help you stay ahead of the curve in the ever-changing world of SEO.

More resources: 


Featured Image: Tapati Rinchumrus/Shutterstock

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Measuring Content Impact Across The Customer Journey

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Measuring Content Impact Across The Customer Journey

Understanding the impact of your content at every touchpoint of the customer journey is essential – but that’s easier said than done. From attracting potential leads to nurturing them into loyal customers, there are many touchpoints to look into.

So how do you identify and take advantage of these opportunities for growth?

Watch this on-demand webinar and learn a comprehensive approach for measuring the value of your content initiatives, so you can optimize resource allocation for maximum impact.

You’ll learn:

  • Fresh methods for measuring your content’s impact.
  • Fascinating insights using first-touch attribution, and how it differs from the usual last-touch perspective.
  • Ways to persuade decision-makers to invest in more content by showcasing its value convincingly.

With Bill Franklin and Oliver Tani of DAC Group, we unravel the nuances of attribution modeling, emphasizing the significance of layering first-touch and last-touch attribution within your measurement strategy. 

Check out these insights to help you craft compelling content tailored to each stage, using an approach rooted in first-hand experience to ensure your content resonates.

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Whether you’re a seasoned marketer or new to content measurement, this webinar promises valuable insights and actionable tactics to elevate your SEO game and optimize your content initiatives for success. 

View the slides below or check out the full webinar for all the details.

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