A new deep learning algorithm called CoupleNet analyzes the tweets of two users and estimates how compatible the duo would be. It’s a different approach than dating sites and apps like Match.com or Tinder that rely on their data and algorithms to predict whether two users will “like” or text each other and then facilitate communication between possible lovebirds. CoupleNet, on the other hand, looks at public social media accounts for both explicit and implicit clues that two people could be romantically suitable.
To train the algorithm, the team behind CoupleNet created a dataset of almost 2 million tweets pulled from over 4,600 likely couples. The “couples” were found by identifying public accounts who sent messages like “good night baby love you :-*” to each other. After training, CoupleNet could identify real couples in the testing data better than other recommendation tools that were given the same data. “To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem,” the researchers write.
CoupleNet could also explain itself. The algorithm was able to spot what mutual interests between users it thought suggested a good match, like tweets about K-pop bands. But it’s more than just word matching. CoupleNet could find pairs correctly even when there are no obvious matching signals. The researchers write that “we believe ‘hidden’ (latent) patterns (such as emotions and personality) of the users are being learned and modeled in order to make recommendations.”
This type of tool could one day be turned into a “Who to follow”-type feature, but for romance. The paper also points out that their method likely overcomes issues that crop up on dating sites, like deceptive self-presentation, harassment, and bots. Meanwhile, tech companies like Facebook and Tinder continue to develop on their own AI-powered matchmaking efforts–and have incredible troves of data to mine. But unlike those carefully guarded datasets, the researchers behind CoupleNet offer a method that anyone can use on publicly available data. They’re also planning on releasing their own data set, so any machine learning-inclined cupids out there can build their own matchmaker tool.