Machine learning enters the online dating world

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As machine learning continues to advance and become more accessible, more applications are starting to take advantage of the technology to better serve experiences to users. Whether it’s chatbots or Snapchat filters, machine learning is naturally finding places to fit in.

Take for example the new dating app called Hily. As a response to many of the dating apps on the market that exchange meaningful insights with potential partners for geographical convenience, Hily bases their platform on using user data to recommend matches. Yes, this is something many traditional dating platforms use, but Hily is taking it a step farther by using machine learning to better serve results the more you use the app.

Before the AI really kicks in, the platform allows users to either manually enter information about themselves or connect with social platforms to fill in information that can be relevant in finding a match. This gives you a nice base, and is used to get you matched with people initially. As you continue to use the app, Hily’s algorithms will start adapting to find what it feels are better matches. It analyzes who you’re swiping left and right to better match you with people, and once matches are made it looks at interactions, depth of those conversations, and media shares with your matches.

While this can seem intrusive, no humans are looking at this data, and it is simply compiled into the machine learning algorithms to better serve you matches. In addition to the machine learning, Hily is also working to make sure bots and fake accounts are kept to a minimum by making users go through a verification process before making connections. While there will never be a perfect solution to spam on dating apps, it definitely helps alleviate the issue a bit.

There will always be a place for the Tinders of the world, but for those looking at more meaningful engagements (whether long term, making friends, or just hooking up), something like Hily is helping bridge that gap.

Originally published at on September 26, 2017.

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