Applying the Netflix recommendation algorithm to dating

In your online journeys you have probably encountered recommendation algorithms. Amazon will tell you that Customers who viewed this item also viewed. YouTube will automatically queue up another video that it thinks you'll like. Instagram will suggest new people to follow.

Many of these algorithms use an approach called collaborative filtering, where recommendations are based on the selections of people who have similar tastes to you. The best known of these techniques is matrix factorization, used by the team that won the Netflix Prize in 2009 — which is why it's often called the Netflix recommendation algorithm.

Dating recommendations

Could collaborative filtering be used for online dating, to recommend people that you'd be attracted to? There's no reason why not.

There's a spreadsheet floating around with data from dozens of speed dating events. With a bit of effort you can apply collaborative filtering to answer the question: given the first N rounds of yes/no answers, what will people decide in round N+1?

The predictions are surprisingly accurate. After a few rounds you can predict whether a person will say yes or no with 65-80% accuracy (for the data science nerds: the precision and recall values are in the same range). And you can do this without knowing the characteristics of the participants (their age, height, etc).

So, for speed dating at least, the Netflix recommendation algorithm works really well. We might like to believe that dating preferences are unique and complicated, but it turns out they're actually fairly predictable.

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