The word algorithm has deeper roots than most realize, that is because it derives from the mediaeval Islamic mathematician Muhammad ibn Musa al-Khwarizmi whose name was historically Latinized as Algoritmi. It was he who first posited and systematized the concept of an algorithm as a procedure or set of rules used for determining results or answers.
Over the intervening millennium since the word first gained currency it has seldom left the realm of academic mathematics – that was, until the term began to be employed to describe the smart filtering processes cropping up online from the mid-00s onwards.
Nowadays it’s a household term, and one that has acquired a (not entirely undeserved) suspect reputation. That’s because algorithms power virtually every part of our online lives today, and in so doing have created nearly as many novel problems as they’ve solved.
But irrespective of your view of algorithms, with 328.77 million terabytes of new data being produced every day, they have become indispensable in helping us sift through this ocean of information to find what we want, need and enjoy.
Interestingly, why one may suppose there is one single type of algorithm, the reality is more complex. While it’s true in an abstract sense, all algorithms do the same type of job, these can be subdivided into meaningful categories – with different algorithms specialized for use in certain applications.
Here we’re going to take a glance at these different algorithms – as well as some noteworthy examples where deferring to the robot may not be the best course of action.
Algorithms of this type restrict and streamline the data they surface based on user preferences. We are most familiar with these when it comes to our social media lives. Everyone knows the experience of going down a rabbit hole on a specific subject or interest on Instagram or TikTok, only to suddenly have the app only show you content related to that topic. That’s because it has determined that you must be interested in it, and thus filters its vast library of content to favor that interest.
In general, this works well though – as these platforms can predict with reasonable accuracy what our interests are, and as people typically engage with the content that interests them, the algorithm curates a feed of cat videos, rock climbing photos or recipes according to taste.
While algorithms are excellent at sorting objective data into boxes that help humans make decisions, there are certain situations where they are not considered preferable. One glaring example of this is when it comes to sectors or concerns that benefit from cultivating a sense of reliability and trust. These range from insurance platforms, to more mundane examples like online casino gaming. What these two share in common, of course, is an onus on real money deposits, and an outside chance of malicious actors if using an improperly vetting service. For this reason, online casino recommendation services like CasinoBonusCA have become increasingly popular of late, responding to expressions of interest by gamers for reliable recommendations in the sector.
Platforms like these not only utilize human experts in the field to review and vet platforms, but they also furnish patrons with quality bonuses, deals and sign-up offers. In so doing, gaming aficionados can not only depend on the recommendations provided, but can make meaningful savings in the process.
Most commonly used for making recommendations to users, these are the types of algorithm you most commonly find on platforms such as streaming services like Netflix or Spotify, as well as on e-commerce services like Amazon.
These algorithms analyses the behavior of their user base and use this to draw comparisons and make predictions on what certain people will like to see. If many people who like horror films enjoy a specific title, and Netflix has determined that you are also a fan of the genre, it will likely recommend this film to you. The same goes for songs or artists on Spotify.
At first glance this process may look distinct on a web store, but it’s really the same idea. Amazon knows that people who buy sleeping bags normally also want to buy, for example, camping stoves, and will recommend these products to you.