What we see, what we buy and what we think are increasingly out of our control, and are instead being chosen by computer algorithms.
Algorithms, mathematical number crunchers that decide on the order in which web pages, products and status updates are presented to us online, have become evermore sophisticated over time.
When Google first launched its now-ubiquitous search engine, the results one person saw were essentially the same as any other. Now, the results each person sees are based on hundreds of different signals that have been weighted by Google’s PageRank algorithm and are increasingly tailored to each individual user.
This strategy has seen Google grow into a $75-billion behemoth, with the money largely coming from its ability to pair adverts with what people are searching for, driven by that self-same algorithm.
Other companies have also recognised the potential of algorithms to boost the bottom line. The products you see recommended on Amazon are the result of what is called an item-to-item collaborative filtering algorithm, essentially showing you what other customers bought together in the belief that you will like them too. It is believed that some 35 per cent of Amazon’s revenues are driven by this recommendation algorithm.
As a result of the success of Google, Amazon and others, hundreds of retailers now use search algorithms to personalise their results. They use Amazon’s A9 algorithm expertise as well as technology from other search technology providers, such as SLI Systems, which powers hundreds of e-commerce sites, guiding website visitors around sites and making personalised recommendations.
If you visit a site powered by search technology such as SLI’s, it typically works like this: on your first visit when they know nothing about you, it will show products based on popularity among other users. Once you have started searching using keywords, it will show the most popular for those. If it knows you have purchased something from the site, or indeed elsewhere on the web before, it will use that information to finesse the results.
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