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Google’s New Technology Helps Create Powerful Ranking-Algorithms

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Google’s New Technology Helps Create Powerful Ranking-Algorithms

Google has announced the release of improved technology that makes it easier and faster to research and develop new algorithms that can be deployed quickly.

This gives Google the ability to rapidly create new anti-spam algorithms, improved natural language processing and ranking related algorithms and be able to get them into production faster than ever.

Improved TF-Ranking Coincides with Dates of Recent Google Updates

This is of interest because Google has rolled out several spam fighting algorithms and two core algorithm updates in June and July 2021. Those developments directly followed the May 2021 publication of this new technology.

The timing could be coincidental but considering everything that the new version of Keras-based TF-Ranking does, it may be important to familiarize oneself with it in order to understand why Google has increased the pace of releasing new ranking-related algorithm updates.

New Version of Keras-based TF-Ranking

Google announced a new version of TF-Ranking that can be used to improve neural learning to rank algorithms as well as natural language processing algorithms like BERT.

It’s a powerful way to create new algorithms and to amplify existing ones, so to speak, and to do it in a way that is incredibly fast.

TensorFlow Ranking

According to Google, TensorFlow is a machine learning platform.

In a YouTube video from 2019, the first version of TensorFlow Ranking was described as:

“The first open source deep learning library for learning to rank (LTR) at scale.”

The innovation of the original TF-Ranking platform was that it changed how relevant documents were ranked.

Previously relevant documents were compared to each other in what is called pairwise ranking. The probability of one document being relevant to a query was compared to the probability of another item.

This was a comparison between pairs of documents and not a comparison of the entire list.

The innovation of TF-Ranking is that it enabled the comparison of the entire list of documents at a time, which is called multi-item scoring. This approach allows better ranking decisions.

Improved TF-Ranking Allows Fast Development of Powerful New Algorithms

Google’s article published on their AI Blog says that the new TF-Ranking is a major release that makes it easier than ever to set up learning to rank (LTR) models and get them into live production faster.

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This means that Google can create new algorithms and add them to search faster than ever.

The article states:

“Our native Keras ranking model has a brand-new workflow design, including a flexible ModelBuilder, a DatasetBuilder to set up training data, and a Pipeline to train the model with the provided dataset.

These components make building a customized LTR model easier than ever, and facilitate rapid exploration of new model structures for production and research.”

TF-Ranking BERT

When an article or research paper states that the results were marginally better, offers caveats and states that more research was needed, that is an indication that the algorithm under discussion might not be in use because it’s not ready or a dead-end.

That is not the case of TFR-BERT, a combination of TF-Ranking and BERT.

BERT is a machine learning approach to natural language processing. It’s a way to to understand search queries and web page content.

BERT is one of the most important updates to Google and Bing in the last few years.

The article states that combining TF-R with BERT to optimize the ordering of list inputs generated “significant improvements.”

This statement that the results were significant is important because it raises the probability that something like this is currently in use.

The implication is that Keras-based TF-Ranking made BERT more powerful.

According to Google:

“Our experience shows that this TFR-BERT architecture delivers significant improvements in pretrained language model performance, leading to state-of-the-art performance for several popular ranking tasks…”

TF-Ranking and GAMs

There’s another kind of algorithm, called Generalized Additive Models (GAMs), that TF-Ranking also improves and makes an even more powerful version than the original.

One of the things that makes this algorithm important is that it is transparent in that everything that goes into generating the ranking can be seen and understood.

Google explained the importance for transparency like this:

“Transparency and interpretability are important factors in deploying LTR models in ranking systems that can be involved in determining the outcomes of processes such as loan eligibility assessment, advertisement targeting, or guiding medical treatment decisions.

In such cases, the contribution of each individual feature to the final ranking should be examinable and understandable to ensure transparency, accountability and fairness of the outcomes.”

The problem with GAMs is that it wasn’t known how to apply this technology to ranking type problems.

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In order to solve this problem and be able to use GAMs in a ranking setting, TF-Ranking was used to create neural ranking Generalized Additive Models (GAMs) that is more open to how web pages are ranked.

Google calls this, Interpretable Learning-to-Rank.

Here’s what the Google AI article says:

“To this end, we have developed a neural ranking GAM — an extension of generalized additive models to ranking problems.

Unlike standard GAMs, a neural ranking GAM can take into account both the features of the ranked items and the context features (e.g., query or user profile) to derive an interpretable, compact model.

For example, in the figure below, using a neural ranking GAM makes visible how distance, price, and relevance, in the context of a given user device, contribute to the final ranking of the hotel.

Neural ranking GAMs are now available as a part of TF-Ranking…”

GAMS Hotel Search Query Ranking Example

jag frågade Jeff Coyle, co-founder of AI content optimization technology MarketMuse (@MarketMuseCo), about TF-Ranking and GAMs.

Jeffrey, who has a computer science background as well as decades of experience in search marketing, noted that GAMs is an important technology and improving it was an important event.

Mr. Coyle shared:

“I’ve spent significant time researching the neural ranking GAMs innovation and the possible impact on context analysis (for queries) which has been a long-term goal of Google’s scoring teams.

Neural RankGAM and related technologies are deadly weapons for personalization (notably user data and context info, like location) and for intent analysis.

Med keras_dnn_tfrecord.py available as a public example, we get a glimpse at the innovation at a basic level.

I recommend that everyone check out that code.”

Outperforming Gradient Boosted Decision Trees (BTDT)

Beating the standard in an algorithm is important because it means that the new approach is an achievement that improves the quality of search results.

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In this case the standard is gradient boosted decision trees (GBDTs), a machine learning technique that has several advantages.

But Google also explains that GBDTs also have disadvantages:

“GBDTs cannot be directly applied to large discrete feature spaces, such as raw document text. They are also, in general, less scalable than neural ranking models.”

In a research paper titled, Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? the researchers state that neural learning to rank models are “by a large margin inferior” to… tree-based implementations.

Google’s researchers used the new Keras-based TF-Ranking to produce what they called, Data Augmented Self-Attentive Latent Cross (DASALC) modell.

DASALC is important because it is able to match or surpass the current state of the art baselines:

“Our models are able to perform comparatively with the strong tree-based baseline, while outperforming recently published neural learning to rank methods by a large margin. Our results also serve as a benchmark for neural learning to rank models.”

Keras-based TF-Ranking Speeds Development of Ranking Algorithms

The important takeaway is that this new system speeds up the research and development of new ranking systems, which includes identifying spam to rank them out of the search results.

The article concludes:

“All in all, we believe that the new Keras-based TF-Ranking version will make it easier to conduct neural LTR research and deploy production-grade ranking systems.”

Google has been innovating at an increasingly faster rate these past few months, with several spam algorithm updates and two core algorithm updates over the course of two months.

These new technologies may be why Google has been rolling out so many new algorithms to improve spam fighting and ranking websites in general.

Citat

Google AI Blog Article
Advances in TF-Ranking

Google’s New DASALC Algorithm
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees?

Official TensorFlow Website

TensorFlow Ranking v0.4.0 GitHub page
https://github.com/tensorflow/ranking/releases/tag/v0.4.0

Keras Example keras_dnn_tfrecord.py

Searchenginejournal.com

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Google Bard vs. ChatGPT: vilken är den bättre AI-chatboten?

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Google Bard vs. ChatGPT: vilken är den bättre AI-chatboten?

Google Bard och ChatGPT are two of the most prominent artificial intelligence (AI) chatbots available in 2023. But which is better? Both offer natural language responses to natural language inputs, using machine learning and millions of data points to craft useful, informative responses. Most of the time. These AI tools aren’t perfect yet, but they point to an exciting future of AI assistant search and learning tools that will make information all the more readily available.

As similar as these chatbots are, they also have some distinct differences. Here’s how ChatGPT and Google Bard measure up against one another.

Which is better, Google Bard or ChatGPT?

This is a tricky question to answer, as at the time of writing, you can only use Google Bard if you’re part of a select group of early beta testers. As for its competition, you can use ChatGPT right now, completely for free. You may have to contend with a waitlist, but if you want to skip that, there’s a paid-for Plus version offering those interested in a more complete tool the option of paying for the privilege.

Still, when Google Bard becomes more widely available, it should offer credible competition for ChatGPT. Both use natural language models — Google Bard uses Google’s internal LaMDA (Language Model for Dialogue Applications), whereas ChatGPT uses an older GPT-3 language model. Google Bard bases its responses to questions on more recent data, with ChatGPT mainly trained on data that was available prior to 2021. This is similar to how Microsoft’s Bing Chat works.

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We’ll have to reserve judgment on which is the more capable AI chatbot until we get time to play with Google Bard ourselves, but it looks set to be a close contest when it is more readily available.

Are Google Bard and ChatGPT available yet?

As mentioned, ChatGPT is available in free and paid-for tiers. You might have to sit in a queue for the free version for a while, but anyone can play around with its capabilities.

Google Bard is currently only available to limited beta testers and is not available to the wider public.

Banner of Google Bard intro från 6 februari.

What’s the difference between Google Bard and ChatGPT?

ChatGPT and Google Bard are very similar natural language AI chatbots, but they have some differences, and are designed to be used in slightly different ways — at least for now. ChatGPT has been used for answering direct questions with direct answers, mostly correctly, but it’s caused a lot of consternation among white collar workers, like writers, SEO advisors, and copy editors, since it has also demonstrated an impressive ability to write creatively — even if it has faced a few problems with accuracy and plagiarism.

Still, Microsoft has integrated ChatGPT into its Bing search engine to give users the ability to ask direct questions of the search engine, rather than searching for terms of keywords to find the best results. It has also built it into its Teams communications tool, and it’s coming to the Edge browser in a limited form. The Opera browser has also pledged to integrate ChatGPT in the future.

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ChatGPT Google Bard
Accessible through ChatGPT site. Only text responses are returned via queries. Integrated with Google Search. You only need to change a Google setting to get your regular search results when using Google Bard AI, and vice versa.
ChatGPT produces answers from its trained database from 2021 and before. Google Apprentice Bard AI will be able to answer real-time questions.
Based on GPT (Generative Pre-trained Transformer). Based on LaMDA (Language Model for Dialogue Applications).
Service has a free and paid plan option (called ChatGPT Plus). Service is free.
Has built-in plagiarism tool called GPT-2 Output Detector. No built-in plagiarism detection tool.
Available now Still in beta test phase

Google Bard was mainly designed around augmenting Google’s own search tool, however it is also destined to become an automated support tool for businesses without the funds to pay for human support teams. It will be offered to customers through a trained AI responder. It is likely to be integrated into the Chrome browser and its Chromium derivatives before long. Google is also expected to open up Google Bard to third-party developers in the future.

Under the hood, Google Bard uses Google’s LaMDA language model, while ChatGPT uses its own GPT3 model. ChatGPT is based on slightly older data, restricted in its current GPT3 model to data collected prior to 2022, while Google Bard is built on data provided on recent years too. However, that doesn’t necessarily make it more accurate, as Google Bard has faced problems with incorrect answers to questions, even in its initial unveiling.

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ChatGPT also has a built-in plagiarism checker, while Google Bard does not, but Google Bard doesn’t have the creative applications of ChatGPT just yet.

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Google to pay $391.5 million settlement over location tracking, state AGs say

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Google to pay $391.5 million settlement over location tracking, state AGs say

Google has agreed to pay a $391.5 million settlement to 40 states to resolve accusations that it tracked people’s locations in violation of state laws, including snooping on consumers’ whereabouts even after they told the tech behemoth to bug off.

Louisiana Attorney General Jeff Landry said it is time for Big Tech to recognize state laws that limit data collection efforts.

“I have been ringing the alarm bell on big tech for years, and this is why,” Mr. Landry, a Republican, said in a statement Monday. “Citizens must be able to make informed decisions about what information they release to big tech.”

The attorneys general said the investigation resulted in the largest-ever multistate privacy settlement. Connecticut Attorney General William Tong, a Democrat, said Google’s penalty is a “historic win for consumers.”

“Location data is among the most sensitive and valuable personal information Google collects, and there are so many reasons why a consumer may opt out of tracking,” Mr. Tong said. “Our investigation found that Google continued to collect this personal information even after consumers told them not to. That is an unacceptable invasion of consumer privacy, and a violation of state law.”

Location tracking can help tech companies sell digital ads to marketers looking to connect with consumers within their vicinity. It’s another tool in a data-gathering toolkit that generates more than $200 billion in annual ad revenue for Google, accounting for most of the profits pouring into the coffers of its corporate parent, Alphabet, which has a market value of $1.2 trillion.

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The settlement is part of a series of legal challenges to Big Tech in the U.S. and around the world, which include consumer protection and antitrust lawsuits.

Though Google, based in Mountain View, California, said it fixed the problems several years ago, the company’s critics remained skeptical. State attorneys general who also have tussled with Google have questioned whether the tech company will follow through on its commitments.

The states aren’t dialing back their scrutiny of Google’s empire.

Last month, Texas Attorney General Ken Paxton said he was filing a lawsuit over reports that Google unlawfully collected millions of Texans’ biometric data such as “voiceprints and records of face geometry.”

The states began investigating Google’s location tracking after The Associated Press reported in 2018 that Android devices and iPhones were storing location data despite the activation of privacy settings intended to prevent the company from following along.

Arizona Attorney General Mark Brnovich went after the company in May 2020. The state’s lawsuit charged that the company had defrauded its users by misleading them into believing they could keep their whereabouts private by turning off location tracking in the settings of their software.

Arizona settled its case with Google for $85 million last month. By then, attorneys general in several other states and the District of Columbia had pounced with their own lawsuits seeking to hold Google accountable.

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Along with the hefty penalty, the state attorneys general said, Google must not hide key information about location tracking, must give users detailed information about the types of location tracking information Google collects, and must show additional information to people when users turn location-related account settings to “off.”

States will receive differing sums from the settlement. Mr. Landry’s office said Louisiana would receive more than $12.7 million, and Mr. Tong’s office said Connecticut would collect more than $6.5 million.

The financial penalty will not cripple Google’s business. The company raked in $69 billion in revenue for the third quarter of 2022, according to reports, yielding about $13.9 billion in profit.

Google downplayed its location-tracking tools Monday and said it changed the products at issue long ago.

“Consistent with improvements we’ve made in recent years, we have settled this investigation which was based on outdated product policies that we changed years ago,” Google spokesman Jose Castaneda said in a statement.

Google product managers Marlo McGriff and David Monsees defended their company’s Search and Maps products’ usage of location information.

“Location information lets us offer you a more helpful experience when you use our products,” the two men wrote on Google’s blog. “From Google Maps’ driving directions that show you how to avoid traffic to Google Search surfacing local restaurants and letting you know how busy they are, location information helps connect experiences across Google to what’s most relevant and useful.”

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The blog post touted transparency tools and auto-delete controls that Google has developed in recent years and said the private browsing Incognito mode prevents Google Maps from saving an account’s search history.

Mr. McGriff and Mr. Monsees said Google would make changes to its products as part of the settlement. The changes include simplifying the process for deleting location data, updating the method to set up an account and revamping information hubs.

“We’ll provide a new control that allows users to easily turn off their Location History and Web & App Activity settings and delete their past data in one simple flow,” Mr. McGriff and Mr. Monsees wrote. “We’ll also continue deleting Location History data for users who have not recently contributed new Location History data to their account.”

• This article is based in part on wire service reports.

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5 Tips to Boost Your Holiday Search Strategy

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Student skriver på dator

With the global economic downturn, inflation, ongoing supply chain challenges, and uncertainty due to the Ukraine war, this year’s holiday shopping season promises to be very challenging. Will people be in the mood to spend despite the gloom? Or will they rein in their enthusiasm and save for the year ahead?

With these issues in mind, here are five considerations to support your search engine optimization strategy this holiday shopping season:

1. Start early.

Rising prices are likely to mean shoppers will start researching their holiday spending earlier than ever to nab the best bargains. Therefore, retailers must roll out their holiday product and category pages — and launch any promotions — sooner to ensure their pages get crawled and indexed by search engines in good time.

Vissa e-handelsbutiker lyckas få sina sidor att rankas tidigt genom att uppdatera och återanvända samma del av webbplatsen för högtidsinnehåll och kampanjer, rotera mellan innehåll för jul, mors dag, alla hjärtans presenter, försäljning av fjärde juli, etc. Detta tillvägagångssätt kan hjälpa dig att behålla den fart, länkar och auktoritet du bygger upp med Google och få dina semestersidor synliga och rankas snabbt.

2. Gör forskning till en ännu större prioritet.

Med all osäkerhet i år är det viktigt att använda SEO-forskning för att identifiera trendiga säsongsbetonade nyckelord och sökfraser i din detaljhandelskategori – och sedan optimera innehållet därefter.

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Med verktyg som Google Trender kan du extrahera användbara insikter baserat på vilka typer av sökningar människor gör. Till exempel, med många modeåterförsäljare som nu tar betalt för produktreturer, kommer prioritering av sökord som "gratis retur" att få mer sökkraft? Och när pengarna är snävare, kommer konsumenterna att hålla fast vid varumärken de litar på snarare än något nytt – vilket betyder att varumärkessökningar kan vara högre?

3. Använd Google Shopping mer.

För att få ut det mesta av sina semesterutgifter är det mer sannolikt att konsumenter vänder sig till onlinemarknadsplatser som Google Shopping eftersom de gör det lättare att jämföra produkter, funktioner och priser, samt att identifiera de bästa erbjudandena både online och i närliggande butiker .

Använd därför ett kombinerat tillvägagångssätt som inkluderar listning i Google Shopping och samtidigt optimera produktdetaljsidor på din e-handelssida för att säkerställa att de är unika och ger mer värde än konkurrenternas sidor. Var exakt med produktnamn på Google Shopping (innehåller namnen t.ex. de ord som folk söker efter?); se till att du tillhandahåller all nödvändig information som Google kräver; och sätt ett pris som inte är alltför långt från konkurrenterna. 

4. Ge andra sökkällor den uppmärksamhet de förtjänar.

Tidigare i år Google själv erkänd att konsumenter – särskilt yngre konsumenter – börjar använda TikTok, Instagram och andra sociala medier för sökning. Faktiskt, forskning föreslår att 11 procent av produktsökningarna nu börjar på TikTok och 15 procent på Instagram. Särskilt yngre konsumenter är mer engagerade av visuellt innehåll, vilket kan förklara varför de anammar visuellt fokuserade sociala webbplatser för sökning. Så, som en del av din sökstrategi, skapa och dela innehåll på populära sociala medier som dina målkunder besöker.

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På samma sätt, med människor som börjar sina shoppingsökningar på marknadsplatser som Amazon.com, bör optimering av alla listor du har på webbplatsen vara en del av din strategi. Och tack och lov, ju bättre optimerade dina produktdetaljsidor är för Amazon (med unikt, användbart innehåll), desto bättre kommer de att rankas på Google också!

5. Håll betald budget för sena tillfällen.

Den större osäkerheten och volatiliteten den här semesterperioden innebär att du måste hålla ett öga på kundernas beteende och vara redo att omfamna möjligheter som dyker upp senare. Att få höga organiska rankningar för sena kampanjer är alltid mer utmanande, så håll tillbaka en del av den betalda sökbudgeten för att hjälpa till att driva trafik till dessa sidor – till exempel via Google Ads. Viktiga sökord att inkludera i sensäsongens sökannonskampanjer inkluderar "leverans före jul" och "leverans samma dag". För lokalt inriktade sökannonser kan du överväga att "hämta när som helst före jul".

Utsikten till en tuff, oförutsägbar shoppingsäsong för helgerna innebär att sökteam måste lansera säsongsbetonade SEO-planer tidigt, noggrant spåra shoppares beteende och vara redo att anpassa sig när saker och ting förändras.

Marcus Pentzek är chefs SEO-konsult på Sökmetrics, den globala leverantören av sökdata, mjukvara och konsultlösningar.

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