Artificial Intelligence Machine Learning SEO

Conduct Deep Learning Optimization to Meet User Intent

How to Conduct Deep Learning Optimization to Meet User Intent

Updated 2.2.2022

To align with where search trends in deep learning, start by having a user-focused approach to what should be on your web pages.

Artificial Intelligence and deep learning are quickly changing how industries like healthcare and financial services are successful in the online space. Deep Learning optimization is now a core topic in the Machine Learning community that seeks to keep up with the latest search techniques using Google datasets. The long-term benefits of highly structured pages built with organized data will offer your business better results in search rankings.

We’re living in exciting times; it is inspiring to see what deep learning is brought to online business! Modern machine learning approaches, such as deep learning, are the beginning of the future of search. The phenomenal growth of the web and computer science in deep learning means that many businesses still are focusing on what formerly worked to reach buyers, but are still missing the concepts of SEO structured data. Let’s cover some basic terms and get started.

Table of Contents

What is DeepMind for Google?

The DeepMind for Google goes by the acronym DMG. Its team implements DeepMind’s cutting-edge Machine Learning research to Google products and Google Search infrastructure. Millions of people use it for asking questions and conducting search queries without knowing or understanding the technology behind it. It is behind the scenes of current computer learning algorithms.

It is useful to help businesses win visibility in SERPs and sales.

How is supervised learning different from unsupervised computer learning?

In supervised learning, a computer is trained to predict human-designated labels, such as type of tree-based labeled tree pictures; unsupervised learning is not dependent on labels. It’s capable of making its own prediction workflow such as attempting to predict each successive word in a particular sentence. Reinforcement learning permits an agent to discover action sequences that optimize its total benefits, such as winning games and predicting elections, without explicit examples of good techniques, enabling autonomy.

How does Google use Deep Learning?

One example of how Google uses Deep Learning is Google Maps. Aggregate location data can be used to understand traffic, historical user data, and live traffic conditions. Using machine learning to generate predictions helps someone using Google Maps to navigate. They can learn whether the road traffic along their business route is heavy or light, an estimated travel time, and the estimated time of arrival (ETA).

It’s Google Assistant speech recognition AI uses deep neural networks to train its algorithm how to better understand spoken commands and questions. The system was updated to function on a new platform called Google Neural Machine Translation; this involved moving everything to a deep learning environment.

However, there are many ways the tech giant uses this technology – many that only their employees know of.

Websites Must Offer What Consumers Prefer

Your online business presence and revenue merit continual audits, reflection, planning, and preparation for how to stay current in
creating web content that users want to consume
. But it also gives digital marketers a chance to improve approaches and seize new opportunities in both earned and paid search differently.

Naturally, the continual study goes into knowing in advance what user questions will be surfacing and what content to be preparing, which may be a major step towards general intelligence that your business needs. Deep learning is proving that creating better content in long-form articles generates more user engagement and sales than producing more content of less value. says, “The idea of using GPUs for training deep learning algorithms was first proposed in 2009”. It has since conducted many experiments to understand how different deep learning optimization algorithms perform and offers ongoing training. Additionally, the annual Searchmetrics Ranking Factors whitepaper** makes a stunning statement that traditional web ranking factors have become irrelevant.

For many, the updates in how SEO works this year is new rocket science that involves the use of deep learning. We understand the hours it requires to determine the impact on your business domain, and love helping Minneapolis business grow their online presence.

Search marketing experts aren’t only using essential Structured Data markup as described at and on the Google Developers pages to earn information on rich Knowledge panels and rich snippets. These are multiple sources that expound on how search works and where it will be growing for improved future use. It is evolving fast and worth paying a lot more attention to.

Are Ranking Factors Dead?

Technical SEO factors remain important and should be resolved on a domain to maintain a healthy SEO optimization score. The evolving search landscape and the sheer increase in the volume of searches create a bigger job for search engines. Marketers must quickly understand, and implement a better search strategy to be competitive in desktop, mobile, voice and image search. If your site’s coding is outdated, broken, or bloated, you shouldn’t expect search engines to favor your site in search rankings.

Every site faces the same fact, the quality, relevance, correctness, and uniqueness of your content is internalized and judged by Google, along with the number of relevant backlinks. FatJoe talks about the new page experiece by Google**; which underscores this. If your content is deemed not the best answer, you won’t be rewarded with higher rankings and better visibility in SERPs. You can gain a lot of direct brand visibility by showing up in People Also Ask boxes. The newer approach is more about content relevance and meeting user demands. For example, if your web pages are bogged with slow loading pixels, it is a technical ranking factor. Users that don’t wait due to problems with load speed are increasing in numbers.


Here is the exact list of the top insights of the new ranking factors that SearchMetrics discovered:

  • Google’s deep learning algorithm now adapts to queries and operates in real time.
  • Content Relevance is a new ranking factor —and it’s the driving force behind garnering top rankings.
  • Technical factors remain a prerequisite for good rankings, but these elements alone are no longer enough.
  • Backlinks are now simply one of many contributing factors; high rankings are even possible for link-free websites.

Deep Learning and Graph Databases Help Users Locate Information

Another factor in creating high-quality content is a shift to providing a new post or article based on a topic versus being keyword themed. Everything you publish online becomes part of your Knowledge Graph and demonstrates your industry expertise, which subjects you are an authority on, and what relevant content you have produced to prove it.

The increased investment in deep learning, Artificial Intelligence, and machine learning all center on content relevance that supersedes the old-fashioned approaches to SEO and PPC campaign optimization. Predicting consumer behavior patterns using deep learning algorithms is now easier than ever.

Site visitors should be able to thrive on the solutions you offer that solve complex problems. Graph databases and deep learning help users find who said what on their search topics. And beyond that, it should include just when it was said, what channel features it, and where they said it. From there, users hopefully call you, fill out a form, connect on social channels, complete a shopping cart purchase, drive right to your store, or take some other positive action step to connect.

Be wary of placing too much emphasis on keyword rankings as this metric in search results is shifting to entity-based SERPs. Google’s choice for producing search results vary based on a number of assorted criteria.

Two take precedence in our experience:

• Geolocation. User search results are adjusted according to the longitude and latitude from where they are at just then. This impacts not only local search and Google Maps Marketing, but additional aspects of organic search rankings as well. While “near me” is commonly part of search queries, even if not, GoogleBot will naturally display results near you. One tactic that is really driving local SEO for some sites is implementing Local Business schema markup.

• Established Personal Preferences. All major search engines continually stock-pile information on how you and others use the web. This forms a pattern call personalized search. This information is stored and cloud and becomes big data used to offer more relevant search results. Your GPS records, browser history, tweets, and much more shape how the SERPs seek to match your preferences.

SEO is Becoming More About Content Relevance

Businesses that find improvements in their website rankings discover that it is due to pages with fantastic unique content relevance and that also offers the best solution to users. This is a key finding from SearchMetric’s latest study. Relevant content refers to how well the information on a website corresponds to a search query. Relevance criteria include content elements like the visible text, as well as images or videos. In addition, relevance can be generated through meta elements like title, meta description and alt tags.

Marketers that comprehend the various forms of search queries (informational, navigational and transactional) can create new content and page structure around them. Useful information centers on the user experience and what the user wants to accomplish, learn, or buy. User signals are the real leaders, especially in local search marketing.

The importance of context within your content needs to match a searcher’s intent. BERT and MUM are deep learning algorithms which is related to natural language processing and decipher intent more easily.

Deep Learning Provides Searchers Better Answers

Google offers choices for search queries that it believes are the best answer and will provide the best user experience. Depending on the nature of the query, semantic search optimization may help you win features like an answer box for a medical search, a video how-to tutorial, or an in-depth research article.

In the past, Google has been more elusive. In a streamed live event on Mar 23, 2016, Google confirmed the top three ranking signals as being content, links and RankBrain. Andrey Lipattsev, a Search Quality Senior Strategist at Google talked about RankBrain is where machine and deep learning come into play and function in real time.

We see this in practice in the Google Knowledge Graph question answer section.

Paradigm Shift in Ranking Factors by Industry

Content is no longer about a ton of pages and posts. With the proliferation of web content, a bigger site may negatively absorb your crawl budget. More relevant, tailored-to-fit the user content offered in the form of a how-to post, tips, infographics, or image galleries offering examples of visual work, such as for a historic house painter, are great. Concise lists with clear bulleted or numbered steps make it easy for readers who need to follow instructions.

This arrangement of informational elements is an integral part of a site’s UX design and should be well-formatted and marked up with schema. Structured data is not meant to be a ranking factor, but as it helps organize your page’s content, that factor alone contributes to its chances for higher rankings.

SEO is definitely becoming more complex. For individuals who once practiced a one-size-fits-all approach, the call to change is more drastic. Different industries have unique criteria for ranking factors to gain improved visibility in relevant search results. There is no tidy one-factor approach to successful marketing strategies. However, one thing is clear, your site needs valid industry information that is well structured.

Searchmetrics introduced that it will begin publishing industry-specific whitepapers for businesses in the Health, Finance, e-Commerce, Media, and Travel sectors. The manifold complexity in search ranking factors, and how they’re applied to user queries involves a long-term commitment to understanding which factors affect rankings based on your niche, competition, and user expectation.

How to Optimize for Deep Learning?

Prepare for deep learning to become part of your website optimization process by:

1. Centralize your data. Whether your company is big or small, it takes a certain mindset to get value from deep learning and know-how to optimize for it. To begin, create a “data lake” or data graph to help you more easily access, compare and analyze critical inputs of user actions. Fortunately, more and more sources are capable of handling an enormous amount of data and helping webmasters, PPC account managers, and SEO professionals make sense of it. An in-depth SEO Site Audit can contribute to ways to leverage Deep Learning to provide more value to your readers.

2. Make it the culture of your organization. The websites that take the lead today and in the future required a well-planned process. No longer does it work to just create the next post that inspires your writing pen. Success means failing at times, but failing forward. Run experiments quickly on infrastructure that supports rapid, iterative development; test, measure, and tweak. Advance your organizational culture from the point of making decisions based on emotional hunches to relying more on proven data insights. Then your web content and UX ontology structure can be read better by machine learning and perform better in deep learning applications.

3. Stay fluid and Create Ever-Green Content. The faster you learn deep-learning concepts and how to implement them when creating new web content the sooner you will be offering more awesome user experiences. Build-in time for research, taking new courses, running experiments and sharing your results in the forums. Consider joining Andrew Ng’s course via Coursera, or participating in a Kaggle contest.

We recommend diving into learning how knowledge graphs improve generative AI content.

“Deep Learning is a new area of research in Machine Learning which aims to achieve its original goal: Artificial Intelligence.” – Amrita School of Engineering ***

“It (machine learning) has started to produce astounding results. Deep learning is not just for making efficient anti-spam systems anymore; now it’s powering self-driving cars, walking robots, human-level speech recognition and synthesis and much, much more. This is an amazing development!” -Machine learning consultant Aurélien Géron****

“The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. We show that more sophisticated off-the-shelf optimization methods such as Limited memory BFGS and Conjugate gradient with line search can significantly simplify and speed up the process of pretraining deep algorithms. – Andrew NG*

What are Deep Learning Native Parallel Graph Databases?How to Conduct Deep Learning Optimization to Meet User Intent

Deep Learning Native Parallel Graph Databases offer data for deeper insights and better outcomes. Machine learning, even as it advances under the umbrella of deep learning, remains computationally demanding, and graph-based machine learning is no exception. With every new point of entity connection or level of connected data, the volume of data in every search enlarges exponentially. This then requires massively parallel computation to crisscross the data and better understand user search intent.

Key-value databases need proper connection but with a clean and reduced number off separate lookups or RDBMS to avoid too many slow joins. Some standard graph database can end up struggling to handle deep link analytics on large graphs. A native graph database that manages massively parallel and distributed processing is best.

In order to compute and explain the reasons behind personalized search suggestions and fraud discovery, the graph database should have a powerful query language that can not only traverse the connections in the graph but also support computation such as filtering and aggregation and multifaceted data structures to recall the evidence. Conceptual search requires a structured approach; it supports both search engine and user’s ease of use.

What is NSL in TensorFlow?

Neural Structured Learning, or NSL, in TensorFlow isn’t as complex as it sounds. Its a framework for training deep neural networks that rely on structured signals along with specific inputs. This learning model leverages Neural Graph Learning to accomplish training neural networks using graphs and structured data. These are generated by multiple sources such as knowledge graphs, medical records, genomic data, or multimodal relations. This also support the natural language processing used in the BERT algorithm.

How does NSL use Graphs and Structured Data?

Structured data contains rich entity-relationship data among the samples. During the training phase of an ML model, engaging the structured signals assists in reaching higher model accuracy. The structured signals bring a better method and consistency to the training of a neural network. This is achieved by forcing the model to assimilate precise predictions as well as maintaining the input structural similarity. This is tremendously useful to search marketing strategists as they approve which SEO techniques are best.

It is no easy challenge for today’s digital marketer to decipher specific user intent. By setting hunches aside and leveraging big data-rich with key insights you can adopt a new approach. Solid optimization techniques remain foundational to ecommerce best practices to improve sales and adhering to Google’s Search Guidelines is a prerequisite for good rankings. Your UX architecture, structured schema implementation, relevant linking structure, and fast site speed continue to be applicable.

Traditional ranking factors are no longer able to help you remain competitive. New algorithms, new use of voice search, more devices, coupled with deep learning and its capacity to analyze website content and understand user intent in real-time are here to stay. Hill Web Creations can help remove levels of confusion or a sense of being overwhelmed. Your website can thrive with a structured approach to content like it never has before and function more efficiently to match user intent.

The user experience in the professional SEO’s world is only increasing. Improving the user satisfaction metrics on your website and prioritizing what works to increase your SERP click rate will increase levels of relevant traffic and conversions. The factors we have mentioned in this article strongly correlate with better rankings as well. Google has consistently stated that what they are seeking is to provide the search results individuals want. It is as simple as the fact that you, like all Internet users, click and dwell on content you want.

Summing up Thoughts: Deep Learning Optimization

At Hill Web Marketing, we love what we do. We could talk about and explain what SEO is all day! We don’t apologize for our passion for technical SEO expertise; it means having the ability to offer you unique professional insights to improve customer experiences on your site. Follow our search marketing blog for fresh and trusted news.

If you would like to know more about our achievements in helping business websites rank better, let’s talk in person. We may suggest starting with a Schema Audit for Fixes and Opportunities






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