We've all heard the terms "machine learning" and "learning algorithms", it'll be useful to any SEO or content writer to know... because they're the ones that'll be doing a lot of the teaching.
First, it's important to remember that an algorithm is simply a mathematical procedure. Don't expect to see true artificial intelligence emerge anytime soon. However, a mathematical algorithm can notice patterns and those patterns can be analyzed for probabilities. That, you've been seeing online for some time. Think patterns.
Types of Machine Learning Processes
Depending upon what the programmer is focusing upon, different approaches can be taken. Even in search, there can be unusual focuses.
The most common approaches are statistical reasoning and inductive reasoning.
Arithmetical analysis
Statistical reasoning simply gathers data and analyzes the probabilities of potential occurrences following prior observed results.
For example, if an algorithm observes that in 80 percent of observed instances, ravens are black, it will extrapolate from that the chance that 80 percent of future observed ravens will also be black.
Inductive analysis
Inductive analysis is somewhat similar, in that it also involves extrapolating probabilities, but it's geared toward proving or disproving a exact theory. For example, if the theory is that a startled dog will snap at a threat, it will gather results from tests and either confirm or disgrace that theory, based upon the preponderance of experiential results.
Neither arithmetical nor inductive reasoning models allow for the inclusion of random results, until they represent a significant percentage of the observed results. At that point, the patterns they define can become a factor in a arithmetical model, whereas in an inductive model, they will only effect findings ultimately.
How This Plays Into Search Engine Algorithms
Suppose, for instance, that a given algorithm is designed to determine how valid the SERPs are to an inquiry. It might look at a factor such as bounce rate as an pointer of validity. If users don't split the bounce threshold, the result could be deemed relevant to the query, indicating that the ranking algorithm was exact. That would be utilizing statistical reasoning.
On the other hand, an algorithm designed to detect purchased links could look at such things as other pages which are linked to from a source page, where those other purpose pages have been found to be buying links, which could establish some level of probability that your page had also purchased a link from that site. It would then look at other signals which would corroborate that probability or discredit it.
Do we know whether any of the search engines really use these models in those scenarios? No, of course not. It's just one of many possibilities. What is more important in our context is, what might a machine learning algorithm be able to learn, in either case?
The statistical model is quite obvious. Once the algorithm decides that 80 percent of ravens are likely to be black, it can simply weight its predictions accordingly; a purely probabilistic weighting, based upon statistics.
In inductive models, the process is a little more subtle. naturally, there would be a number of other signals, each with their own weighting, which can vary, depending upon their prevalence. That implies a very non-linear probability curve which may be considerably more complex.
In the example above, such things as how many other target sites linked to from the source page are suspected of buying links, the chance weighting of those suspicions, the past history of all sites involved in the analysis and a host of other signals. Combined in a mathematical formula, a probability factor can be here at which could put the site over a preset threshold, triggering a dampening or a punishment.
Machine Learning Applied to Search Queries and Ranking
For instance, a simple willpower of whether a query or an on-page phrase is negative or positive can often be reached. More complex characteristics like sarcasm, satire or humor, however, are still largely beyond a machine's understanding. But a search query for [bad example of customer service] will render first page results with the terms "bad", "poor" and "worst" in the content, title and/or URL.
This may seem to be a simple example of recognizing synonyms for the search query, but remember that synonym recognition was just one of the early baby steps in search. Looking at a subtly different search query of yields such pages as:
So "not good" is equated with "bad" by the algorithm. A baby step, perhaps, but a step, however. Rest assured that Google didn't physically enter all such possible relationships - there are far too many. This is much better accomplished by developing an algorithm that will continuously adjust its lexicon as it detects patterns.
Machine Learning & Your Content
The algorithms learn from the patterns they notice, both in queries and documents, as well as the relationships they find out between them. That's why writing content using a broader selection of terms .
• It provides new syntax in an exact context, which can aid the algorithms' learning process.
• It also enables you to write content that is more theoretical – directed to the reader, rather than to the search engines.
One end result is a more rapid development of complex sympathetic by machines – the heart of semantics. It also helps you provide content that is more informative, amusing, and engaging for your readers.
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