If you’re one of Google Discover’s millions of daily users, you’ve doubtless come across all manner of content without actively searching for it. A Spanish user, for instance, might see headlines promising to reveal the secret recipes of avant-garde chef Ferràn Adrià, what time 46 year-old TV presenter Pilar Rubio goes to bed, the latest unmissable deals from Lidl, or the new car that will allow them to say “adiós” to annual inspections.
We still tend to think of Google as a search engine first and foremost. The iconic search bar on a white background – Alphabet’s flagship product that underpins its substantial advertising revenue – has long been part of our vocabulary, as both a noun and a verb that has the final say in contentious debates.
But over the years, Google has established another service, one that moves beyond traditional “searching” to algorithmic “finding”. Google Discover, its content recommendation system, has a similar interface to a social media feed, though without comments, retweets or visible interactions from other users.
Unlike other Google services like Maps, Drive or Photos, Discover does not have its own app. Instead, it comes pre-installed on Android phones as an integral part of the interface, usually accessed by swiping right on the home screen.
The big question regarding this and any other recommendation system – from controversial algorithms like those on Instagram and Facebook, to Netflix, YouTube and even seemingly innocuous services like Vinted – is how it decides what content to recommend to you. What have you, as a user, done to deserve this on your phone?
No magic formula
When people try to explain how platforms recommend content to us, they tend to lean on algorithmic imaginaries, gossip or unfounded beliefs. Some even attempt to train or manipulate the algorithm based on these reconstructions.
However, the truth is that there is no single algorithm or grand equation that you can control. Recommendation systems like Google Discover are powered by a vast, opaque network of algorithmic infrastructure, a circuit of computer instructions hidden inside a metaphorical black box about which Google has revealed very few details.
Everything else that we know comes from either professional efforts to learn how it works, or from scientific literature, which has noted its impact on fields like journalism.
However, these systems’ ingredients cannot simply be read like the label on a bottle of ketchup or shampoo.
Funnelling content
Google’s webpage defines Discover as a personalised feed based on the user’s interests, determined by their activity on the web and in apps. Scientific studies published by Google’s own engineers have revealed it to be an industrial-scale content recommendation system, while independent academic research places it at the intersection between information retrieval and algorithmic curation.
While we do not know exactly how it works, we can explain why Google Discover shows you certain content by looking at the typical architecture of a recommendation system, along with the evidence and hypotheses we have about Google’s product.
A useful metaphor is that of a funnel divided into stages. Every day, millions of pieces of content of all kinds are published online, from articles and long-form videos to social media posts and AI summaries. Discover distils this immense variety into a list tailored specifically to every single user.
The first filters
As with Google Search, the first step is indexing. Google maintains a database where it crawls the internet and applies an initial filter to exclude anything that does not comply with its policies. This ensures that content which breaches security rules is excluded from the outset. This includes, for instance, posts seeking to recruit people to a terrorist organisation, or graphic images without any journalistic context.
However, getting past this filter merely means that content is eligible. It does not guarantee it a place in your feed.
To narrow down this initial pool, the system then identifies which content is most likely to match your interests. Google Discover does this by identifying which specific elements (known as entities) are present in your searches or interactions – such as Ferràn Adriá, Pilar Rubio and Lidl – and linking them together. In this way, it can interpret which seemingly unrelated searches or articles form part of the same interest, and organise them into broader topics and subtopics.
Once it has profiled your interests, the system needs to translate them into a language in which it can compare and find related content on a large scale. In the case of Google Discover, analysts such as Damien Andell have suggested that it uses mathematical representations known as embeddings.
These vectors convert groups of entities (known as clusters) and your own interests into coordinates on a single board that resembles a game of Battleships. The closer two points are, the greater their affinity.
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Tracking clicks, likes and zooms
Once these clusters have been identified, the funnel narrows again into predictive models. Here, the system’s AI attempts to predict how you will react to each of the options.
Google’s engineers describe eleven predictive objectives, seven of which are specified: five positive (clicking, liking, responding positively to a survey, clicking to view details, and clicking to expand text) and two negative (dismissing or permanently blocking the content).
With all of these predictions on the table, the system then processes them to rank the candidates. The usual approach is to combine them into a single score, giving greater weight to some signals than others. One article may, for instance, have a high probability of you clicking on it, but also a high probability of you blocking it; another may arouse less immediate curiosity, but generate more positive signals.
Discover has to resolve this trade-off to decide what appears first and what appears next. However, Google has not disclosed how it combines these predictions.
Once the candidates have been ranked, some systems apply a final filter to incorporate criteria such as diversity, recency or fairness. This prevents the top five pieces of content from all being about, for example, the same unmissable new gadget from Lidl.
An endless loop
We’re now at the end of the funnel, and Google Discover has decided what to show you. But the process doesn’t stop there. The system feeds back on itself, observing the signals you generate both explicitly (when you block a source or like a piece of content) and implicitly (whether you stop to read an article or move on to the next one).
This new data is fed back into the system to train and update the model. Recommendations generate interactions, interactions produce new data, and that data feeds into future recommendations.
We cannot know exactly what mix of signals leads to Ferràn Adrià, Pilar Rubio, or any other name appearing in your Discover feed. What we do know is that the system is a never-ending game of chance, one that feeds on your history and location, as well as small, everyday behaviours that, while imperceptible to you, provide the data that fuels this vast recommendation system.
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This article was originally published on The Conversation. Read the original article.
