Click me

Inside Machine Learning for SEO

SENTIENT SEARCH INSIDE MACHINE LEARNING FOR SEO Every day, many hopeful search engine optimization (SEO) experts attempt to conquer the seemingly impossible: reroute the Google algorithm backwards to deliver those ever-important higher search rankings. Machine learning is that extra process push in the form of a structured, analytical tool that allows for greater understanding of SEO insight and an awareness of underlying search engine algorithms. As is the case with many structures, however, there is always a weak point. Here, we look at the strengths and weaknesses of Machine Learning Systems (MLS) in SEO. THE BLUEPRINT MACHINE LEARNING Machine learning (ML) refers to how a software-based algorithm is made to automate the learning process behind something normally judged or decided by humans. ML enables learning and decision-making processes to be scaled up, and the analysis of vast data sets with hundreds of underlying variables is possible. In order to make this possible, it is necessary to collect large data sets and analyse them statistically in one of two ways. These methods can be broadly separated into regression and classification. REGRESSION Regression relates to the prediction, or forecasting, of real outcomes; a hypothesis is generated given the output of a learning algorithm on a set of gathered training data. A typical use of this might be the prediction of how a site's search engine ranking is likely to be affected given the increased or reduced inclusion of certain website content. CLASSIFICATION Classification, on the other hand, relates to an arbitrary assignment of elements within a data set to two or more defined types. For example, the classification of the demographic of a website user CLASSIFICATION as a young, up-and-coming hipster or a tech-savvy grandmother. Many SEO firms subscribe to the philosophy of “Why construct a team of engineers to track queries and check each algorithm when we can just write a program for it?" While the time gain is desirable, the technical process is extremely complicated. AT A BASIC LEVEL, A MACHINE LEARNING SYSTEM IDEALLY WORKS AS FOLLOWS: The MLS will train itself, by comparing input variables to outputs on a finite training set comprising a large amount of data gathered from a website. This "training" data may be labeled, or unlabeled (i.e. unstructured). The search engine begins to compile data, while simultaneously adjusting itself. DATA Machine learning software then receives feedback and raises or Based on the hypothesis provided by its newly-trained learning algorithm, lowers the importance of certain parts of the system as part of its actions. the MLS will be able to make a future prediction or classify gathered data elements into certain types. GHOSTS IN THE MACHINE Much like any other machine, from the automobile to the iPhone, SEO machine learning systems are not perfect. MECHANISTIC FLAWS WITH A WEB-BASED MACHINE LEARNING ALGORITHM INCLUDE: The addition of new variables over Rapid formula changes. ll time, such as social signals, visitor data, links, and bounce rate. This can be partially addressed, however, by structuring the problem as "unsupervised" – i.e. with no pre-defined labels for the underlying factors. Multiple algorithms operate at different times in different parts of the world, thus providing instability in tracking. Inability to monitor external influences (for example user fads, trends and global events, unrelated to the behaviour of search engine algorithms). These simply aren't encapsulated in data gathered from a webpage. Simply put, machine learning is a good way to determine a more rigorously educated guess of future site user behaviour, or to classify the users themselves into categories. It will never reverse-engineer the Google algorithm, however, nor provide a “be all, end all" how-to guide on increased search rankings. Depending on your point of view, this news is either a blessing or a curse. If you consider great search content to be the name of the game, then you win. APPLYING MACHINE L EARNING TO SEO For those with sites with continuously changing content, machine learning can be used effectively to monitor search engine algorithms' actions. DOCUMENTING FAVOURITISM OF CERTAIN BEHAVIOURS OVER OTHERS IS USEFUL FOR ENTERPRISE SITES SUCH AS NEWSPAPERS OR E-ZINES WITH DISCOUNTS. USEFUL VARIABLES TO MONITOR INCLUDE: WELCOME AESTHETIC WEB DESIGN CHANGES CONTENT LINKS LANDING PAGE CONTENT TECHNOLOGY IN USE NAVIGATION Machine learning for SEO is also effective during static search engine updates. During these updates, your site (and those in the same query space) may have non-spurious data that can be used to evaluate factors that have positive and negative impacts. Google and Russian search engine Yandex are among the many currently using machine learning systems. It may not be perfect, and the continuously changing algorithm factors add new variables into the equation, but machine learning allows for a better idea of which content arrangement increases rankings. SOURCES: ALCHEMY VIRAL – LINK RESEARCH TOOL DATA: THE MISSING ANALYSIS | alchemy.VIRAL SEARCH ENGINE WATCH - HOW SEARCH ENGINES USE MACHINE LEARNING FOR PATTERN DETECTION | SEO-THEORY.COM – THE THEORY OF DEEP WEB INTERFEROMETRY | ALCHEMY VIRAL - MACHINE LEARNING WEBSITES

Inside Machine Learning for SEO

shared by ColumnFive on May 09
1 comment
Every day, many hopeful search engine optimisation (SEO) experts attempt to conquer the seemingly impossible: reroute the Google algorithm backwards to deliver those ever-important higher search ranki...


Did you work on this visual? Claim credit!

Get a Quote

Embed Code

For hosted site:

Click the code to copy


Click the code to copy
Customize size