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While they are more likely to respond to first contact from someone who matches their original wish list, we found that 70% of the messages sent by women and 55% of those sent by men were to communicate with people who did no meet their original criteria.
Given that we have a tendency to make choices other than what we have predicted for ourselves in our stated preferences, this is a much more dynamic method.
Online dating is becoming a multi-billion-dollar industry so sites need to be better than ever at suggesting potential dates for their users.
We think that collaborative filtering algorithms are a good option for this.
We devised a reciprocal recommendation system to match users of mutual interest according to content-based algorithms, which are based on factors that include a user’s age, education and income – and another, collaborative filtering-style algorithm, which is based on prior communications: both of the user and of other users with similar interests and attractiveness. Collaborative filtering algorithms not only learn the preferences of an online dater but also take information from the behaviour of other similar users.
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While women with a larger number of photos are more likely to get messages and replies to their communications, the number of photos on a man’s profile has little bearing on the number of contacts or replies he receives.