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Information Retrieval: An Introduction For SEOs

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Information Retrieval: An Introduction For SEOs

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

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

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

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

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

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

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

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

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

Vector Space Models

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

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

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

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

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

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

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

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

To Google, [dan taylor] can be:

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

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

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

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

Similarity Matching

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

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

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

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

Cosine is measured between -1 and 1.

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

The Role Of An Index

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

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

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

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

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

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

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

Accuracy & Post-Retrieval

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

You can watch his presentation here.

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

These are precision and recall.

Precision

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

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

Recall

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

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

Conclusion

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

For example:

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

More resources: 


Featured Image: BRO.vector/Shutterstock



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LinkedIn Rolls Out 4 Updates For Business Pages

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LinkedIn Rolls Out 4 Updates For Business Pages

LinkedIn is rolling out an array of new features for business pages.

These updates can help you showcase your business’s brand identity, values, and offerings while utilizing advanced publishing and community-building tools.

Learn how these innovative features can enhance your LinkedIn marketing efforts.

1. Update To Scheduled Posts

One of the new features lets you plan your business page posts up to three months ahead for steady interaction with followers.

Screenshot from: LinkedIn, March 2023.

Available now on desktop, this feature will come to mobile soon.

2. Audio Events

LinkedIn is introducing live, audio-only discussions, eliminating reliance on external broadcasting applications.

LinkedIn Rolls Out 4 Updates For Business PagesScreenshot from: LinkedIn, March 2023.

This versatile, casual approach fosters audience connections and can position your organization as an industry authority.

Listeners can engage in the discussion through emojis and request to speak if they wish to contribute verbally.

3. Automatic Job Posting

For businesses with fewer than 1,000 employees, LinkedIn now offers an automatic job posting feature.

LinkedIn Rolls Out 4 Updates For Business PagesScreenshot from: LinkedIn, March 2023.

Once activated, the platform will automatically share one open role daily as a pre-scheduled post. The posts can be edited after they’re shared.

The feature excludes what LinkedIn categorizes as “basic jobs.”

4. Following Pages As A Page

LinkedIn Pages can now follow other Pages, making it easier to join chats related to your field with a feed dedicated to content from the businesses you’re following.

This feature aims to help businesses work together, share ideas, and create strong online communities of professionals.

In Summary

LinkedIn’s latest features for business pages offer new options to share content, connect with people, attract new talent, and keep up with industry chatter.

By leveraging these tools, you can improve your B2B marketing efforts and strengthen your online presence.



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Top 3 Ways To Build Authority By Going Beyond Just Link Building

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Top 3 Ways To Build Authority By Going Beyond Just Link Building

You want your online business to thrive. One of the best ways to do this is to establish website authority – and the key to successful authority building is to increase trust with your audience.

With the rise of AI tools, you must publish high-quality content that stands out from your competition, who may be using tools like ChatGPT.

On March 15, I moderated a webinar with Sabrina Hipps, VP of Partner Development, and Jeremy Rivera, Director of Content Analysis at CopyPress.

Hipps and Rivera demonstrated how content promotion, link building, and authentic subject matter expertise could help you rank higher on SERPs and elevate your online authority.

Here’s a summary of the webinar. To access the entire presentation, complete the form.

1. Create Unique Content With First-Hand Experience – Avoid AI, The “Fancy Parrot”

In the world of content creation, where good content creators are showing their expertise, there are certain key things AI can’t do.

  • AI can’t have first-person experience. They can’t think for themselves the same way humans can.
  • If the AI follows a generative model, and it can’t yet distinguish the truth. If you fact-check some of the information, you’ll find it doesn’t exist.

The counter to AI content is unique content that shows this truth, expertise, and first-hand experience.

[Learn how this helps build your authority] Instantly access the webinar →

2. Highlight Quality Authorship

High-quality content encompasses everything from accuracy and mistake-free writing to clearly displaying expertise.

Ensure Your Content Is Error-Free

In many cases, low-quality content, or posts with false information and repetitive issues, can lead to being devalued on SERPs or accidentally containing duplicate content.

Image created by CopyPress, March 2023

Add More “E” To EAT – Experience

The Issue: To combat low-quality SERPs, Google seeks first-hand experience.

The Solution: Invite a subject matter expert to review the content, check for factual inaccuracies, and add that extra layer of expertise to the content.

Bridge The Write ≠ Expertise Gap

The Issue: It’s important to recognize that the ability to write is not synonymous with expertise; just because someone can write doesn’t mean they are accurate or a subject matter expert.

The Solution: Try pairing a subject matter expert with a strong writer who can interview and interject quotes helps build better content.

Ask Questions

The Issue: Sometimes, you may not have the in-house subject matter experts you need for a piece of content.

The Solution: Conduct outreach to gather expertise to boost your content quality. First, consider what your audience wants to know. Then, generate three to ten questions to ask a professional.

[Learn a tactic that works] Instantly access the webinar →

Tap Social Media

The Issue: Where do you find the professionals you need to interview for your next piece of high-expertise content?

The Solution: With so many experts creating on social media, it’s a great platform to leverage. Here are essential outreach steps you can do:

  • Observe.
  • Participate.
  • Engage.
  • Network.

Doing this can also be considered link-building in another sense. Because link building is marketing, and marketing is about building relationships.

Find Allies Who Are Also Targeting Your Audience

Combining outreach efforts with the Nexus approach helps you create relationships and connections beyond just the link.

[Learn what the Nexus approach is] Instantly access the webinar →

3. Use Other Authority Builders, In Addition To Links

One way to increase brand queries is through influencers, knowledge panel (which becomes part of a brand’s search results), and mentions.

To increase mentions:

  • Use HARO & Terkel.
  • Publish unique industry data.
  • Do something distinctive that stands out.
  • Connect with publishers with significant traffic, not for links but for visibility & mentions.
  • Leverage influencers and industry experts.

[BONUS: Get a step-by-step branded keyword strategy] Instantly access the webinar →

At the end of the day, when you publish unique, relevant, and authoritative content, it gets referenced and cited by others.

[Slides] Discover The Top 3 Ways To Build Authority By Going Beyond Just Link Building

Here’s the presentation:

Join Us For Our Next Webinar!

Google Shopping: 5 Ways AI Can Increase Ecommerce Sales and Profit

Join Malin Blomberg, CEO of Bidbrain and Google Shopping expert, as she shares the best hacks for digital marketers and ecommerce business owners to maximize conversion value.


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Featured Image: Paulo Bobita/Search Engine Journal



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Microsoft Introduces Category-Based Targeting For Search Ads

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Microsoft Introduces Category-Based Targeting For Search Ads

Microsoft has unveiled a new approach to search advertising that aims to help businesses more effectively reach their target audiences in the retail media space.

This innovative category-based targeting solution aims to address the limitations of traditional keyword targeting while leveraging the power of keywords to optimize campaign performance.

Moving Beyond Keyword Targeting

Although keyword targeting has been a cornerstone of search advertising for years, it has limitations.

By focusing solely on keyword targeting, advertisers may miss out on valuable opportunities to promote their products, which can negatively impact a campaign’s performance and limit revenue potential.

Retailers and advertisers are beginning to realize that shoppers browse digital aisles on retailer websites rather than solely searching for specific products using keywords.

As a result, strategies limited to keyword targeting don’t adequately address their needs.

Unlocking The Power Of Category-Based Targeting

Microsoft’s new solution targets shoppers based on their browsing categories, utilizing keywords to boost campaign bids.

This approach allows advertisers to capitalize on both audience behaviors, resulting in a stronger performance.

By boosting bids with keywords, advertisers can increase their chances of converting purchase intent into sales.

Retailers can optimize the site experience for shoppers through product taxonomy, making it easier for customers to find what they want.

Microsoft PromoteIQ’s AI-driven algorithms can then deliver more relevant ads by layering keywords as a booster in addition to categories.

This new approach simplifies campaign management for advertisers, as they only need to test and retain a few high-performing keywords.

For retailers, this efficiency translates into increased demand.

Proven Results: Higher CTR & RPM

Tests have shown that this unique solution delivers impressive results.

Campaigns that utilize category-based targeting and boost bids by keywords have a 320% higher click-through rate (CTR) than campaigns without keyword bid boosting.

Retailers also benefit from this approach, achieving 8x higher revenue per thousand impressions (RPM).

The Future Of Search Advertising?

Microsoft PromoteIQ’s new category-based targeting solution is a significant shift in search advertising.

By addressing the limitations of traditional keyword targeting and maximizing the value of both audience behaviors, this innovative approach can potentially improve performance for advertisers and retailers alike.

As the advertising landscape continues to evolve, embracing solutions like this is crucial for staying ahead and delivering an exceptional shopping experience for customers.


Featured Image: sockagphoto/Shutterstock

Source: Microsoft



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