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Learning user preferences in online dating
Tinder is using machine learning to identify which photos work and which don’t.
It starts out with some A/B testing—swapping the photo first seen by others when your profile is shown on Tinder.
First Met.com, a leading online dating site with over 70 million installs, analyzed over 2.4 million interactions among its current user base in the United States to discover the likelihood of users to respond to other users based on race.
“The big thing we’re learning is the difference between stated preference and actual behavior, and that’s a big deal,” First Data Analyst Josh Fischer said in an interview with USA Today.
[Sydney] : University of Sydney, School of Information Technologies, Pizzato, Luiz. [et al.] University of Sydney, School of Information Technologies [Sydney] 2010 Pizzato, Luiz.
Also, even though users explicitly assign weights, they are often in contrast to the users’ actual behaviors in many cases.
This paper suggests a new matchmaking system called Adaptive Match-Making System (AMMS) that automatically adjusts the weight of each attribute by analyzing the user’s previous behaviors.
Learning user preferences in online dating / Luiz Pizzato ... 2010, Learning user preferences in online dating / Luiz Pizzato ...
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Online dating presents a rich source of information for preference learning.
► Overcoming the cold start problems for newly entered users.
As any veteran of online dating knows, photos can make or break your chances on Tinder.
► Automatically modifying the matchmaking model as the number of sent message records.
► Providing reciprocal matchmaking by taking into account a partner’s preferences.