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AI Re-Ranking For Semantic Search

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AI Re-Ranking For Semantic Search

Search isn’t just about matching keywords – and that’s even more true when we talk about semantic search.

Semantic search is about finding the right information for the searcher at the right time.

That goes beyond finding the right keywords and concepts and speculating how searchers will interact with the results.

Artificial intelligence (AI) re-ranking will take information about the people who come to search and tailor search results to the individual.

That might be done on a cohort level, changing results based on trends, seasonality, and popularity.

It might also be done individually, changing results based on the current searcher’s desires.

While AI re-ranking is not easy to implement in a search engine, it brings outsized value for conversions and searcher satisfaction.

Re-Ranking With Artificial Intelligence

AI-driven re-ranking can improve search results, no matter the underlying ranking algorithm a search engine uses.

That’s because good search results are more than textual relevance and business metrics like raw popularity.

Good results take into account other signals and do so on a per-query level.

To see why this is important, let’s focus on the business metric of popularity.

It’s a good general ranking signal but can fall short for specific queries. A search query of “red dress” might bring up in the first results two different dresses: “backless dress with red accents” and “summer dress in bright red.”

The backless dress might be more popular as an overall dress and product.

But in this case, specifically, it’s not what customers want.

They want a red dress, not one with red accents, and they click and buy accordingly.

Shouldn’t the search engine take that as a signal to rank the summer dress higher?

Search Analytics

As the above example shows: Understanding what searchers are doing is necessary for re-ranking.

The two most common events to track are clicks and conversions.

Generally, those are the only two events necessary and must be events coming from search.

The example above also highlights another important consideration: the events should be tied to specific queries.

That allows the search engine to learn from the interplay between the different result sets and user interactions. It propels the summer dress higher in the search results for the “red dress” query.

The same product might be less popular for other queries than its neighbors.

When looking at your different events, you’ll want to weigh them differently, too.

Clicking on a result is a sign of interest while making a purchase (or any other conversion metric) is a sign of commitment.

The ranking should reflect that.

The weighting doesn’t need to be complex.

You can go as simple as saying that conversions are worth double clicks.

You should test the right ratio for your own search.

You may also want to discount events based on the result ranking at the time the searcher saw it.

We know that a result’s position influences its clickthrough rate (CTR).

Without discounting events, you may have a situation where the top results become even more entrenched because they get more interactions, which keep them ranked higher – and repeating infinitely.

Freshness And Seasonality

A simple way to combat this self-reinforcing loop is by discounting events based on the time passed since the event.

That happens because each event that occurred in the past has an increasingly small impact on re-ranking. That is, until, at some point, it has no impact at all.

For example, you might divide the impact of each event by two, each day, for 30 days. And after 30 days, stop using the event for ranking.

A nice benefit of using freshness in the re-ranking algorithm is that it also introduces seasonality into the results.

Not only do you stop recommending videos that were extremely popular years ago but are boring to people today; you also will recommend “learn how to swim” videos in the summer, and “learn to ski” videos in the winter.

YouTube has seasonality and freshness built into its algorithm precisely for this purpose.

Using Signals To Re-rank

Now that you’ve got the signals and decaying them over time, you can apply them to the search results.

When we see “artificial intelligence,” we often think of something incredibly complex and inscrutable.

AI, though, can also be as simple as taking data over time and using it to make decisions, like we’re doing here.

One easy approach is to take a certain number of results and simply re-rank them based on a score.

For performance reasons, this number of results will generally be fairly small (10, maybe 20). Then, rank them by score.

As we discussed above, the score could be as simple as adding up the number of conversions times two, plus the number of clicks.

Adding a decay function makes for more complexity, as does discounting based on result position – but the same general principle applies.

Learning To Rank

A drawback of this re-ranking system is that you are limited to re-ranking a smaller number of results.

If you have a result that would otherwise be popular but isn’t ranking high, that result won’t get the attention it warrants.

This system also requires events on the records and the queries you want to re-rank.

It won’t work for brand new product launches or user-generated content (UGC) that often comes in and out of the search index.

Learning to rank (LTR) can address these issues.

Much like the re-ranking we’ve discussed above, LTR also works based on the idea that the records searchers interact with are better than the ones they don’t.

The previous re-ranking method works by boosting or burying results directly when tied to a specific query.

Meanwhile, LTR is much more flexible. It works by boosting or burying results based on other popular results.

LTR uses machine learning to understand which queries are similar (e.g., “video games” and “gaming console”).

It can then re-rank results on the less popular queries based on interactions on the more common ones.

LTR doesn’t only generalize on queries; it generalizes on records, too.

The LTR model learns that a certain type of result is popular; for example, the Nintendo Switch game “Legend of Zelda: Breath of the Wild.”

Then, it can start to connect to other similar results (for example, “Legend of Zelda: Skyward Sword”) and boost those.

Why, then, not just use LTR if it appears to be much more powerful than your typical re-ranking and provides more query and record coverage?

(In other words: It generalizes better.)

In short, LTR is much more complex and needs more specialized in-house machine learning (ML) expertise.

Additionally, understanding why certain results are ranked in certain places is more difficult.

With the first type of re-ranking, you could look at the number of clicks and conversions over time for one record compared to another.

Meanwhile, with LTR, you have an ML model that makes connections that may not always be obvious.

(Are “Breath of the Wild” and “Sonic Colors” really all that similar?)

Personalization

While re-ranking works across all searchers, personalization is what it sounds like: personal.

The goal of personalization is to take results that are already relevant and re-rank them based on personal tastes.

While there is a debate on how much web search engines like Google use personalization in their results, personalization often impacts the performance of results in on-site search engines.

It is a useful mechanism for increasing search interactions and conversions from search.

Search Analytics

Just as with re-ranking, personalization depends on understanding how users interact with search results.

By tracking clicks and conversions, you’ll have a clearer idea of the kinds of results that the user wants to see.

One significant difference between re-ranking and personalization on this front is that, depending on your search, you might want to adjust how you apply personalization.

For example, if you sell groceries, you definitely want to recommend previously purchased products.

But if your website sells books, you won’t want to recommend a book that a customer has already bought. Indeed, you may even want to move those books down in the search results.

It’s also true, however, that you shouldn’t push personalization so hard that users only see what they’ve interacted with before.

Search empowers both finding and discovery. So, if they return to the search bar, you should be open to the possibility that they want to see something new.

Don’t rank results exclusively via personalization; make it a mix with other ranking signals.

Just as with re-ranking, personalization also benefits from event decay.

Decreasing the impact of older events makes a search more accurately represent a user’s current tastes.

In a way, you can think of it as personal seasonality.

Personalization Across Users

The kind of personalization we’ve seen so far is based on an individual’s own interactions, but you can also combine it with what others are doing inside search.

This approach shows an outsized impact on situations where the user hasn’t interacted with the items in the search results before.

Because the user doesn’t interact with the search result items, you can’t boost or bury based on past interactions, by definition.

Instead, you can look at users that are similar to the current user and then personalize based on what they have interacted with.

For example, say you have a user who has never come to you for dresses but has purchased many handbags.

Then, you can look for other users who have similar tastes and have also interacted with dresses.

Intuitively, other customers who like the same type of handbags as our searcher should also like the same dresses.

Re-Ranking And Personalization For Discovery

Search is only one example of where re-ranking and personalization can make an impact. You can use these same tools for discovery as well.

The secret is to think of your home page and category pages as search results.

Then, it’s clear that you can use the same tools you use for search and gain the same benefits.

For example, a home page is similar to a search page without a query, isn’t it? And a category landing page sure does look like a search page with a category filter applied to it.

If you add personalization and re-ranking to these pages, they can be less static. They will serve users what they prefer to see, and they can push items higher that are more popular with customers overall.

And don’t worry, personalization and re-ranking can mix with editorial decisions on these pages or inside search.

The best way to handle this is by fixing the desired results in certain places and re-rank around them.

We’ve seen that personalization and re-ranking are two approaches that take user interactions with relevant signals to make search better.

You can let your user base influence the result by using the interactions.

Little by little, these interactions tell the search engine what items should be ranking higher.

Ultimately, searchers benefit from a better search experience, and you benefit from more clicks and conversions.

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AI Content Detection Software: Can They Detect ChatGPT?

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AI Content Detection Software: Can They Detect ChatGPT?

We live in an age when AI technologies are booming, and the world has been taken by storm with the introduction of ChatGPT.

ChatGPT is capable of accomplishing a wide range of tasks, but one that it does particularly well is writing articles. And while there are many obvious benefits to this, it also presents a number of challenges.

In my opinion, the biggest hurdle that AI-generated written content poses for the publishing industry is the spread of misinformation.

ChatGPT, or any other AI tool, may generate articles that may contain factual errors or are just flat-out incorrect.

Imagine someone who has no expertise in medicine starting a medical blog and using ChatGPT to write content for their articles.

Their content may contain errors that can only be identified by professional doctors. And if that blog content starts spreading over social media, or maybe even ranks in Search, it could cause harm to people who read it and take erroneous medical advice.

Another potential challenge ChatGPT poses is how students might leverage it within their written work.

If one can write an essay just by running a prompt (and without having to do any actual work), that greatly diminishes the quality of education – as learning about a subject and expressing your own ideas is key to essay writing.

Even before the introduction of ChatGPT, many publishers were already generating content using AI. And while some honestly disclose it, others may not.

Also, Google recently changed its wording regarding AI-generated content, so that it is not necessarily against the company’s guidelines.

Image from Twitter, November 2022

This is why I decided to try out existing tools to understand where the tech industry is when it comes to detecting content generated by ChatGPT, or AI generally.

I ran the following prompts in ChatGPT to generate written content and then ran those answers through different detection tools.

  • “What is local SEO? Why it is important? Best practices of Local SEO.”
  • “Write an essay about Napoleon Bonaparte invasion of Egypt.”
  • “What are the main differences between iPhone and Samsung galaxy?”

Here is how each tool performed.

1. Writer.com

For the first prompt’s answer, Writer.com fails, identifying ChatGPT’s content as 94% human-generated.

Writer.com resultsScreenshot from writer.com, January 2023

For the second prompt, it worked and detected it as AI-written content.

Writer.com test resultScreenshot from writer.com, January 2023

For the third prompt, it failed again.

Sample ResultScreenshot from writer.com, January 2023

However, when I tested real human-written text, Writer.com did identify it as 100% human-generated very accurately.

2. Copyleaks

Copyleaks did a great job in detecting all three prompts as AI-written.

Sample ResultScreenshot from Copyleaks, January 2023

3. Contentatscale.ai

Contentatscale.ai did a great job in detecting all three prompts as AI-written, even though the first prompt, it gave a 21% human score.

Contentscale.aiScreenshot from Contentscale.ai, January 2023

4. Originality.ai

Originality.ai did a great job on all three prompts, accurately detecting them as AI-written.

Also, when I checked with real human-written text, it did identify it as 100% human-generated, which is essential.

Originality.aiScreenshot from Originality.ai, January 2023

You will notice that Originality.ai doesn’t detect any plagiarism issues. This may change in the future.

Over time, people will use the same prompts to generate AI-written content, likely resulting in a number of very similar answers. When these articles are published, they will then be detected by plagiarism tools.

5. GPTZero

This non-commercial tool was built by Edward Tian, and specifically designed to detect ChatGPT-generated articles. And it did just that for all three prompts, recognizing them as AI-generated.

GPTZeroScreenshot from GPTZero, January 2023

Unlike other tools, it gives a more detailed analysis of detected issues, such as sentence-by-sentence analyses.

sentence by sentence text perplexityScreenshot from GPTZero, January 2023

OpenAI’s AI Text Classifier

And finally, let’s see how OpenAi detects its own generated answers.

For the 1st and 3rd prompts, it detected that there is an AI involved by classifying it as “possibly-AI generated”.

AI Text Classifier. Likely AI-generatedAI Text Classifier. Likely AI-generated

But surprisingly, it failed for the 2nd prompt and classified that as “unlikely AI-generated.” I did play with different prompts and found that, as of the moment, when checking it, few of the above tools detect AI content with higher accuracy than OpenAi’s own tool.

AI Text Classifier. Unlikely AI-generatedAI Text Classifier. Unlikely AI-generated

As of the time of this check, they had released it a day before. I think in the future, they will fine tune it, and it will work much better.

Conclusion

Current AI content generation tools are in good shape and are able to detect ChatGPT-generated content (with varying degrees of success).

It is still possible for someone to generate copy via ChatGPT and then paraphrase that to make it undetectable, but that might require almost as much work as writing from scratch – so the benefits aren’t as immediate.

If you think about ranking an article in Google written by ChatGPT, consider for a moment: If the tools we looked at above were able to recognize them as AI-generated, then for Google, detecting them should be a piece of cake.

On top of that, Google has quality raters who will train their system to recognize AI-written articles even better by manually marking them as they find them.

So, my advice would be not to build your content strategy on ChatGPT-generated content, but use it merely as an assistant tool.

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Five things you need to know about content optimization in 2023

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5 Things You Need To Know About Optimizing Content in 2023

30-second summary:

  • As the content battleground goes through tremendous upheaval, SEO insights will continue to grow in importance
  • ChatGPT can help content marketers get an edge over their competition by efficiently creating and editing high-quality content
  • Making sure your content rank high enough to engage the target audience requires strategic planning and implementation

Google is constantly testing and updating its algorithms in pursuit of the best possible searcher experience. As the search giant explains in its ‘How Search Works’ documentation, that means understanding the intent behind the query and bringing back results that are relevant, high-quality, and accessible for consumers.

As if the constantly shifting search landscape weren’t difficult enough to navigate, content marketers are also contending with an increasingly technology-charged environment. Competitors are upping the stakes with tools and platforms that generate smarter, real-time insights and even make content optimization and personalization on the fly based on audience behavior, location, and data points.

Set-it-and-forget-it content optimization is a thing of the past. Here’s what you need to know to help your content get found, engage your target audience, and convert searchers to customers in 2023.

AI automation going to be integral for content optimization

Technologies-B2B-organizations-use-to-optimize-content

As the content battleground heats up, SEO insights will continue to grow in importance as a key source of intelligence. We’re optimizing content for humans, not search engines, after all – we had better have a solid understanding of what those people need and want.

While I do not advocate automation for full content creation, I believe next year – as resources become stretched automation will have a bigger impact on helping with content optimization of existing content.

CHATGPT

ChatGPT, developed by OpenAI, is a powerful language generation model that leverages the Generative Pre-trained Transformer (GPT) architecture to produce realistic human-like text. With Chat GPT’s wide range of capabilities – from completing sentences and answering questions to generating content ideas or powering research initiatives – it can be an invaluable asset for any Natural Language Processing project.

ChatGPT-for-content

The introduction on ChatGPT has caused considerable debate and explosive amounts of content on the web. With ChatGPT, content marketers can achieve an extra edge over their competition by efficiently creating and editing high-quality content. It offers assistance with generating titles for blog posts, summaries of topics or articles, as well as comprehensive campaigns when targeting a specific audience.

However, it is important to remember that this technology should be used to enhance human creativity rather than completely replacing it.

For many years now AI-powered technology has been helping content marketers and SEOs automate repetitive tasks such as data analysis, scanning for technical issues, and reporting, but that’s just the tip of the iceberg. AI also enables real-time analysis of a greater volume of consumer touchpoints and behavioral data points for smarter, more precise predictive analysis, opportunity forecasting, real-time content recommendations, and more.

With so much data in play and recession concerns already impacting 2023 budgets in many organizations, content marketers will have to do more with less this coming year. You’ll need to carefully balance human creative resources with AI assists where they make sense to stay flexible, agile, and ready to respond to the market.

It’s time to look at your body of content as a whole

Google’s Helpful Content update, which rolled out in August, is a sitewide signal targeting a high proportion of thin, unhelpful, low-quality content. That means the exceptional content on your site won’t rank to their greatest potential if they’re lost in a sea of mediocre, outdated assets.

It might be time for a content reboot – but don’t get carried away. Before you start unpublishing and redirecting blog posts, lean on technology for automated site auditing and see what you can fix up first. AI-assisted technology can help sniff out on-page elements, including page titles and H1 tags, and off-page factors like page speed, redirects, and 404 errors that can support your content refreshing strategy.

Focus on your highest trafficked and most visible pages first, i.e.: those linked from the homepage or main menu. Google’s John Mueller confirmed recently that if the important pages on your website are low quality, it’s bad news for the entire site. There’s no percentage by which this is measured, he said, urging content marketers and SEOs to instead think of what the average user would think when they visit your website.

Take advantage of location-based content optimization opportunities

Consumers crave personalized experiences, and location is your low-hanging fruit. Seasonal weather trends, local events, and holidays all impact your search traffic in various ways and present opportunities for location-based optimization.

AI-assisted technology can help you discover these opportunities and evaluate topical keywords at scale so you can plan content campaigns and promotions that tap into this increased demand when it’s happening.

Make the best possible use of content created for locally relevant campaigns by repurposing and promoting it across your website, local landing pages, social media profiles, and Google Business Profiles for each location. Google Posts, for example, are a fantastic and underutilized tool for enhancing your content’s visibility and interactivity right on the search results page.

Optimize content with conversational & high-volume keywords

Look for conversational and trending terms in your keyword research, too. Top-of-funnel keywords that help generate awareness of the topic and spur conversations in social channels offer great opportunities for promotion. Use hashtags organically and target them in paid content promotion campaigns to dramatically expand your audience.

Conversational keywords are a good opportunity for enhancing that content’s visibility in search, too. Check out the ‘People Also Ask’ results and other featured snippets available on the search results page (SERP) for your keyword terms. Incorporate questions and answers in your content to naturally optimize for these and voice search queries.

SEO-and-creating-content-in-2023

It’s important that you utilize SEO insights and real-time data correctly; you don’t want to be targeting what was trending last month and is already over. AI is a great assist here, as well, as an intelligent tool can be scanning and analyzing constantly, sending recommendations for new content opportunities as they arise.

Consider how you optimize content based on intent and experience

The best content comes from a deep, meaningful understanding of the searcher’s intent. What problem were they experiencing or what need did they have that caused them to seek out your content in the first place? And how does your blog post, ebook, or landing page copy enhance their experience?

Look at the search results page as a doorway to your “home”. How’s your curb appeal? What do potential customers see when they encounter one of your pages in search results? What kind of experience do you offer when they step over the threshold and click through to your website?

The best content meets visitors where they are at with relevant, high-quality information presented in a way that is accessible, fast loading, and easy to digest. This is the case for both short and long form SEO content. Ensure your content contains calls to action designed to give people options and help them discover the next step in their journey versus attempting to sell them on something they may not be ready for yet.

2023, the year of SEO: why brands are leaning in and how to prepare

Conclusion

The audience is king, queen, and the entire court as we head into 2023. SEO and content marketing give you countless opportunities to connect with these people but remember they are a means to an end. Keep searcher intent and audience needs at the heart of every piece of content you create and campaign you plan for the coming year.

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Headings With Hierarchical Structure An “Awesome Idea”

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Headings With Hierarchical Structure An "Awesome Idea"

Google’s John Mueller discussed heading elements with a member of the SEO community where he affirmed the usefulness of using hierarchical structure when using heading elements.

Background Context to What Mueller Said

Heading elements <H1> – <H6> are supposed to be used to indicate what a section of a webpage is about.

Furthermore the heading elements have a ranking order, with the <H1> being the highest rank of importance and the <H6> being the lowest level of importance.

The heading element purpose is to label what a section of content is about.

HTML specifications allow the use of multiple <H1> elements. So, technically, using more than one <H1> is perfectly valid.

Section 4.3.11 of the official HTML specifications states:

“h1–h6 elements have a heading level, which is given by the number in the element’s name.

If a document has one or more headings, at least a single heading within the outline should have a heading level of 1.”

Nevertheless, using more than on <H1> is not considered a best practice.

The Mozilla developer reference page about the use of headings recommends:

“The <h1> to <h6> HTML elements represent six levels of section headings. <h1> is the highest section level and <h6> is the lowest.

…Avoid using multiple <h1> elements on one page

While using multiple <h1> elements on one page is allowed by the HTML standard (as long as they are not nested), this is not considered a best practice. A page should generally have a single <h1> element that describes the content of the page (similar to the document’s <title> element).”

John Mueller has previously said that it doesn’t matter if a webpage uses one <H1> or five <H1> headings.

The point of his statement is that the level of the heading isn’t as important as how they are used, with the best practice being the use of  headings for indicating what a section of content is about.

What Mueller Said on Twitter

A member of the SEO community was joking around and gently ribbed Mueller about using more than one H1.

He tweeted:

The SEO followed up by sharing how he preferred using the best practices for heading elements by using only one <H1>, to denote what the page is about and then using the rest of the headings in order of rank, give a webpage a hierarchical structure.

A Hierarchical structure communicates sections of a webpage and any subsections within each section.

He tweeted:

“I’m too traditional with header elements. (HTML 4 for Life! lol)

I’d still recommend using just one H1 element on a page.

I patiently go back to pages to implement header hierarchy for fun.”

John Mueller tweeted his approval in response:

“I think that’s an awesome idea & a great practice.

Header hierarchy is not just useful to Google, it’s also important for accessibility.

(Google still has to deal with whatever weird things people throw up on the web, but being thoughtful in your work always makes sense.)”

Hierarchical Page Structure

In the early days of SEO, <H1> used to be counted as an important ranking factor, one that was more important than an <H2>.

So, back then, one always put their most important keywords in the <H1> in order to signal to Google that the page was relevant for that keyword.

H1 used to have more ranking power so it was essential to use the <H1> to help rankings.

Google’s algorithm was using keywords as a way to “guess” what a webpage was about.

Keywords in the anchor text, keywords in the title tag and keywords in the <H1> helped Google guess what a page was relevant for.

But nowadays, Google doesn’t have to guess.

It is able to understand what sections of a webpage are about, and consequently, what the entire webpage is about.

Despite those advances, many SEOs still believe that using an <H1> is some kind of magic ranking factor.

Headings are no longer about shouting what keyword you want to rank for.

The role of heading elements are now about telling search engines what a section of content is about.

Each section of a content is generally about something specific.

Heading tags make it easier for search engines to know what a page is about.

And that helps them rank the page for the topic.

And according to the official HTML specifications, that’s technically the proper way to use heading elements.

Lastly, Mueller mentioned a quality of the heading element as a way to better communicate for accessibility reasons, like for people who use screen readers.

The official HTML specifications say:

“Descriptive headings are especially helpful for users who have disabilities that make reading slow and for people with limited short-term memory.

These people benefit when section titles make it possible to predict what each section contains.”

So thank you John Mueller for calling attention to the benefits of using headings with a hierarchical structure, for calling attention to how hierarchical structure is useful for Google and for accessibility.

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