To suggest a book to a customer based on their previous purchases is a single directional process but a dating site needs to match users who might have a mutual interest in each other so they are more likely to hit it off.Now that there are so many sites to choose from, they must have a good system in place or they will simply lose custom to their rivals.
When a user creates a new account on a dating site, they may specify what kind of partner they are looking for.A user might say they are looking for someone in their early 20s with at least a bachelor degree and a certain minimum monthly income.We found that people are in many ways predictable in their dating habits but they also often bend their own rules.To make good matches, sites need to look at this rule bending when making recommendations.Unfortunately, our results show users living up to these stereotypes.
Profile photos affect men and women differently too.
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.
They don’t actually stick as rigorously to their preferences as they might have thought they would though.
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.
In the UK, 9.1m people have used an online dating site and one in every five new committed relationships starts online.