The majority of marketers and webmasters have heard about learning algorithms and machine learning.
An algorithm is a statistical process. Artificial intelligence is not widely used at this time, and it is unlikely it will be used in the near future.
A statistical algorithm is able to detect patterns. These patterns then undergo a statistical analysis for probabilities. These algorithms are used to recognise patterns and make inferences.
Machine Learning Processes
Different machine learning approaches can be used, depending on the focus of the algorithm designer. The most commonly employed algorithms use inductive and statistical reasoning.
Statistical Reasoning
This type of algorithm collects data and then determines the probability of future occurrences based upon results that were observed in the past.
For instance, if the algorithm collects data that shows that 90% of fire engines are red, using this data, it will extrapolate that the chance that future observed fire engines will be red, will also be 90%
Inductive Reasoning
Inductive reasoning has similarities to statistical reasoning since it also uses an extrapolation of probabilities. However, it is focused on disproving or proving specific theories. For instance, if a theory states that the majority of new cars sold are blue, it will collect data that will either discredit or confirm that theory on the basis of a statistical analysis of results that were observed.
Inductive and statistical reasoning methods do not include random results unless they are frequent enough to constitute a significant proportion of the results that have been observed. When a result becomes statistically significant, it will be a factor in a statistical reasoning model, but in an inductive reasoning model, it will effect results indirectly.
How Does This Affect Search Algorithms?
Suppose that an algorithm is created in order to determine the validity of search results for a given query. It may use bounce rate as a validity indicator. If site visitors do not exceed a pre-determined bounce rate threshold, the search result may be determined to be relevant. This would be an indication that the algorithm used for ranking was accurate. That is an example of statistical reasoning.
Another example is an inductive reasoning algorithm which is designed to determine if links have been purchased. It could examine additional pages that have links from a particular source page, where the additional destination pages are already known to be purchasing links. The algorithm would them establish some type of probability that your web page has also bought a link from the same site. The algorithm would then examine additional signals that would serve to discredit or support that probability.
Of course, we don't actually know the exact models used in the search algorithms. These are just some of numerous possibilities. However, we can determine what type of learning a machine algorithm may be capable of.
The statistical learning model is rather straightforward. After an algorithm has determined that 90% of fire engines are red, it will give its predictions a 90% weight. This is a completely probabilistic weighting that is based upon statistics.
Inductive reasoning models utilise a process that is not so straightforward. Most often, there will be several additional signals that each have an assigned weighting, which is variable. This will generate a probability curve that is non-linear and much more complicated.
In the link buying example above, factors such as the number of additional target sites that are linked to from a particular source page that are suspected of purchasing links, the weighting associated with those suspicious sites, the history of the sites being analysed and numerous additional signals may be used. These signals will be used in a formula that will arrive at a probability factor. If a site exceeds a pre-set threshold, this may trigger a penalty or dampening of the site.
Applying Machine Learning To Ranking And Search Queries
What type of things are algorithms capable of learning? Artificial intelligence is currently not feasible or practical. Sentiment analysis may be a possibility.
For example, determining whether a phrase on a page is positive or negative can be determined. Complex characteristics such as humour and irony remain beyond the scope of a machine's ability to comprehend. However, if you execute a search query for bad customer support you will see results with the terms bad, worst, and poor in the title, URL, and/or content.
This is just an instance of synonym recognition, but this was one of a number of steps taken by search algorithms. A little bit different search query such as not good customer support gives results such as not good, bad, and poor. This is a small step, but an additional step, nonetheless. You can be sure that Google is not manually entering all these potential relationships into a database. This is done by creating an algorithm that will make adjustments to its lexicon as patterns are detected.
These patterns are not limited to only positive and negative modifiers. They are used on a large number of varying modifiers.
Your Content And Machine Learning
Algorithms learn by detecting patterns, in documents and search queries. They also learn from the relationships that are found between them. Therefore, writing content that uses a wide selection of varying terms may have the following effect:
- It provides additional syntax within a specific context; this assists the algorithms in their process of learning.
- It allows you to create content that is directed at site visitors and that has a conceptual nature, rather than writing for search engines.
- The end result is faster development of understanding complex terms by machines, which is at the core of semantics.