In 2007, Gavin Potter made headlines when he competed successfully in the Netflix Prize, a $1m competition run by the online movie giant to improve the recommendations its website offered members. Despite competition from teams composed of researchers from telecoms giants and top maths departments, Potter was consistently in the top 10 of the leaderboard. A retired management consultant with a degree in psychology, Potter believed he could predict more about viewers‘ tastes from past behaviour than from the contents of the movies they liked, and his maths worked.
Collaborative filtering works by collecting the preferences of many people, and grouping them into sets of similar users. Because there’s so much data, and so many people, what exactly the thing is that these groups might have in common isn’t always clear to anyone but the algorithm, but it works. The approach was so successful that Tsinonis and Potter created a new company, RecSys, which now supplies some 10 million recommendations a day to thousands of sites. RecSys adjusts its algorithm for the different requirements of each site – what Potter calls the „business rules“ – so for a site such as Lovestruck, which is aimed at busy professionals, the business rules push the recommendations towards those with nearby offices who might want to nip out for a coffee, but the powerful underlying maths is Potter’s. Likewise, while British firm Global Personals provides the infrastructure for some 12,000 niche sites around the world, letting anyone set up and run their own dating website aimed at anyone free lesbian hookup apps from redheads to petrolheads, all 30 million of their users are being matched by RecSys. RecSys is already powering the recommendations for art discovery site ArtFinder, the similar articles search on research database Nature, and the backend to a number of photography websites. Of particular interest to the company is a recommendation system for mental health advice site Big White Wall. Because its users come to the site looking for emotional help, but may well be unsure what exactly it is they are looking for, RecSys might be able to unearth patterns of behaviour new to both patients and doctors, just as it reveals the unspoken and possibly even unconscious proclivities of daters.
Recently, Tarr’s vision has started to become a reality with a new generation of dating services, driven by the smartphone
Back in Harvard in 1966, Jeff Tarr dreamed of a future version of his Operation Match programme which would operate in real time and real space. He envisioned installing hundreds of typewriters all over campus, each one linked to a central „mother computer“. Anyone typing their requirements into such a device would receive „in seconds“ the name of a compatible match who was also free that night.
Suddenly, we don’t need the smart algorithms any more, we just want to know who is nearby. But even these new services sit atop a mountain of data; less like Facebook, and a lot more like Google.
He was contacted by Nick Tsinonis, the founder of a small UK dating site called yesnomayb, who asked him to see if his approach, called collaborative filtering, would work on people as well as films
Tinder, founded in Los Angeles in 2012, is the fastest-growing dating app on mobile phones but its founders don’t like calling it that. According to co-founder and chief marketing officer Justin Mateen, Tinder is „not an online dating app, it’s a social network and discovery tool“.
He also believes that Tinder’s core mechanic, where users swipe through Facebook snapshots of potential matches in the traditional „Hot or Not“ format, is not simple, but more sophisticated: „It’s the dynamic of the pursuer and the pursued, that’s just how humans interact.“ Tinder, however, is much less interested in the science of matching up couples than its predecessors. When asked what they have learned about people from the data they have gathered, Mateen says the thing he is most looking forward to seeing is „the number of matches that a user needs over a period of time before they’re addicted to the product“ – a precursor of Tinder’s expansion into other areas of ecommerce and business relationships.