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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.

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.

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

I asked 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.

With 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.

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) model.

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.

Citations

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

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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.

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.

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.”

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 writing on computer

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.

Some e-commerce stores manage to get their pages ranking early by updating and reusing the same section of the website for holiday content and promotions, rotating between content for Christmas, Mother’s Day, Valentine gifts, Fourth of July sales, etc. This approach can help you retain the momentum, links and authority you build up with Google and get your holiday pages visible and ranking quickly.

2. Make research an even bigger priority.

With all the uncertainty this year, it’s vital to use SEO research to identify the trending seasonal keywords and search phrases in your retail vertical — and then optimize content accordingly.

With tools such as Google Trends you can extract helpful insights based on the types of searches people are making. For example, with many fashion retailers now charging for product returns, will prioritizing keywords such as “free returns” get more search traction? And with money being tighter, will consumers stick with brands they trust rather than anything new — meaning brand searches might be higher?

3. Make greater use of Google Shopping.

To get the most out of their holiday spending, consumers are more likely to turn to online marketplaces such as Google Shopping as they make it easier to compare products, features and prices, as well as to identify the best deals both online and in nearby stores.

Therefore, take a combined approach which includes listing in Google Shopping and at the same time optimizing product detail pages on your e-commerce site to ensure they’re unique and provide more value than competitors’ pages. Be precise with product names on Google Shopping (e.g., do the names contain the words people are searching for?); ensure you provide all the must-have information Google requires; and set a price that’s not too far from the competition. 

4. Give other search sources the attention they deserve.

Earlier this year Google itself acknowledged that consumers — especially younger consumers — are starting to use TikTok, Instagram and other social media sites for search. In fact, research suggests 11 percent of product searches now start on TikTok and 15 percent on Instagram. Younger consumers in particular are more engaged by visual content, which may explain why they’re embracing visually focused social sites for search. So, as part of your search strategy, create and share content on popular social media sites that your target customers visit.

Similarly, with people starting their shopping searches on marketplaces such as Amazon.com, optimizing any listings you have on the site should be part of your strategy. And thankfully, the better optimized your product detail pages are for Amazon (with unique, useful content), the better they will rank on Google as well!

5. Hold paid budget for late opportunities.

The greater uncertainty and volatility this holiday season mean you must keep a close eye on shopper behavior and be ready to embrace opportunities that emerge later on. Getting high organic rankings for late promotions is always more challenging, so hold some paid search budget back to help drive traffic to those pages — via Google Ads, for example. Important keywords to include in late season search ad campaigns include “delivery before Christmas” and “same-day-delivery.” For locally targeted search ads, consider “pick up any time before Christmas.”

The prospect of a tough, unpredictable holiday shopping season means search teams must roll out seasonal SEO plans early, closely track shoppers’ behavior, and be ready to adapt as things change.

Marcus Pentzek is chief SEO consultant at Searchmetrics, the global provider of search data, software and consulting solutions.

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Google Home App Gets an Overhaul, Rolling Out Soon

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Google Home app

Google refreshes its Home app with a slew of new features after launching a new Nest gear. This makes it faster and easier to pair smart devices with Matter, adds customization and personalization options, an enhanced Nest camera experience, and better intercommunication between devices.

This revamped Home app utilizes Google’s Matter smart home standard – launching later this year – especially the Fast Pair functionality. On an Android phone, it will instantly recognize a Matter device and allow you to easily set it up, bypassing the current procedure that is often slow and difficult. Google is also updating its Nest speakers, displays, and routers – to control Matter devices better.

Google Home App New Features

  • Spaces: This feature allows you to control multiple devices in different rooms. Google has listed a few things by room: kitchen, bedroom, living room, etc., although it’s pretty limited right now. Spaces let you organize devices how you see fit. For instance, you can set up a baby monitor in one room and set a different room’s camera to focus on an area the baby often plays. With Spaces, you can categorize these two devices into one Space category called ‘Baby.’

Google Home app Spaces

  • Favorites: This one is pretty self-explanatory. It allows you to make certain gears as a favorite that you frequently use. Doing so will bring those devices into the limelight within the Google Home app for easier access. 

Google Home app

  • Media: Google adds a new media widget at the bottom of your Home feed. This will automatically determine what media is playing in your home and provide you with the appropriate controls as and when needed. There will be song controls if you listen to music on your speakers. There will be television remote controls if you’re watching TV. 

Google probably won’t roll out this Home app makeover anytime soon. But you can try it for yourself in the coming week by enrolling in the public preview, available in select areas.

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