How to Use Machine Learning to Build Your Own Search Ranking Algorithm

Machine Learning for Your Own Search Ranking Algorithm in San Diego, CA

Machine learning is all about identifying patterns in data. When the task at hand is determining how to present the information searchers see online, Google, Bing, and other leading search engines apply the concept of machine learning in a way that’s designed to improve the accuracy of results. However, you may be surprised to know you can also use machine learning to create a search ranking algorithm specifically for your needs. Here’s how, brought to you by the experts at Saba SEO, a premier San Diego SEO company.

Set Your Algorithm Goal

Before you start to build your own search ranking algorithm with machine learning, you have to know exactly why you want to do so. A common reason is to better align products and services with what shows up on search engine results pages (SERPs). Because everyone can evaluate relevance differently, it helps to know what you think is relevant to your target audience.

Gather Data

Machine learning won’t work without data, which can be collected by gathering SERP results and using actual humans to rate those results based on how relevant they are to what’s being searched for. As you continue with this process, you’ll get a set of queries and URLs. Split this data into a training set and a test set. A quality rating will be assigned to queries for both sets so algorithm performance can be measured and evaluated.

Determine Your Model Features

A “feature” refers to characteristics that define each document or piece of content. This information is used to make a prediction about how relevant a document will be to a searcher’s query. Possible features might include:

  • A document’s word count
  • The language your document is written in
  • Document scores based on what’s shown in a link graph
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It’s entirely possible that some features won’t predict the quality or relevance of a search either positively or negatively. Some will also be negative. However, it’s good to have this type of mix so your algorithm can “learn.”

Train Your Algorithm

Ideally, you want a ranking algorithm that maximizes your search engine results page ratings from the set of queries and URLs you prepared with their respective quality ratings. You want results grouped from higher to lower quality ratings.

Make Appropriate Adjustments

When you have a lower rating ranking above a higher one, you’ll have a pairwise error. You’ll have to go through a “rinse and repeat” process as you adjust features until you get the appropriate order.

Evaluate Your Algorithm’s Performance

See how well your ranking algorithm is doing by comparing the training set with the test set. The results you get from each set should line up fairly closely.

Do an Online Evaluation

An evaluation will allow you to see if you’re observing search behaviors that suggest real users are satisfied with the results. As you do this, you’ll learn more about the behavior of your intended online searchers. 

Results are often subjective. For instance, if a searcher goes back to the original search page quickly after visiting your landing page, it could be because the info presented was so good it gave them exactly what they wanted. On the other hand, maybe your linked page didn’t deliver. Even so, each time you evaluate your results and make adjustments, you’ll be learning more about your intended audience.

If you’d like more information on building your own search ranking algorithm, call on the SEO specialists at Saba SEO. As an industry-leading SEO company in San Diego, we have more than a decade of experience in search engine optimization, website design and development, and social media marketing. To learn more about how we can help you enhance your overall SEO strategy, reach out to us today at 858-277-1717.