The internet is home to so much information it’s beyond the capability of the human mind to grasp. At present the total data volume worldwide equates to some 120 zettabytes of new information a day. What’s more, this volume increases by around 25% year on year as technology advances and processing power progressively increases.
Granted, much of this information has little bearing on the lives of the average internet user, but even the data we directly engage with through websites has far outstripped our ability to manage without the assistance of sophisticated algorithms. With the total number of websites online now thought to be around the 1 billion mark, and some 18 million brands providing goods and services, one of the great frontiers of modern marketing and commerce is discoverability.
Naturally, we’re all more than accustomed to using search engines such as Bing and Google in order to quickly and efficiently search for products, goods and services that interest us – but the top ranking results for these queries seldom offer a bespoke solution to what we’re seeking. In light of this, a whole industry has grown up around connecting users with the goods and services they actually want by using what are colloquially known as recommendation engines.
We use these recommendation engines every day without necessarily noticing, as they now make up a key part of how people engage with the masses of data that confront us online. Below we’re going to take a look at what recommendation engines are, how they work, and how different types of recommendation engines can be brought to bear to assist us in finding what we need online, when we need it.
How Recommendation Engines Work
In essence, recommendation engines are algorithmic processes designed to rapidly filter through information to find results that conform to a user’s specific requirements, preferences or demographic profile. Many of the largest brands in the world employ these services to aid users efficiently finding what they’re looking for. Among the most popular examples used today are to be found on Amazon, YouTube, LinkedIn and Netflix. What’s more, recommendation engines can be broadly divided into three types, which we will explore below.
Content Based
One of the major forms of these algorithms we engage with commonly is Content based recommendation engines. These collate items and seek to match them to a user’s interests, profile and preferences.
A popular example of these can be found in the iGaming sector. Here, reputable platforms such as CasinoReviews exist to furnish users with a comprehensive directory of real money casinos for them to peruse. Users can further self-select their preferences among these gaming providers based upon the competitive welcome bonuses and sign-up offers provided by their services, as certain promotions will suit individual gamers more than others.
Collaborative Filtering
Another popular form for recommendation engines utilizes a process known as collaborative filtering. With these, the algorithm uses information gathered from a user’s previous purchases or selections to suggest items they will likely enjoy.
For example, Amazon employs collaborative filtering to suggest additional products to accompany an existing purchase. For example, those who buy a printer will likely want to also purchase some printer ink. Amazon’s A10 search algorithm then automates this process, giving users the option to add ink cartridges to their cart at the click of a button. Similarly, Netflix uses collaborative filtering to suggest new movies or shows to watch based upon a user’s prior viewing history, and that of other viewers with similar interests.
Hybrid
Platforms that employ hybrid recommendation engines combine the benefits of content-based and collaborative filtering algorithms to furnish extremely specific and targeted recommendations. One of the best examples of a hybrid algorithm can be seen at LinkedIn – a business-oriented social media platform and job hunting resource. Its hybrid system suggests potential job postings or career opportunities by matching them with a user’s specific interests and preferences.
It also filters by industry, identifying users with similar professional experience. By combining collaborative filtering and content-based recommendations in this way, LinkedIn can not only connect employers and prospective employees with one another across a specific industry field, but can further sub-select for cultural compatibility, approach, outlook and additional skills that may be of value.
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