(Part 2 of 2)
At Say Allo, we believe the goal of a dating application should be to find quality candidates and empower you to make educated decisions on your swipes.
We started with an algorithm and developed our app around it.
We consider ourselves a hybrid to the aforementioned dating app styles:
We believe the best reflection of a user is in their actions. In this case, that is their likes and dislikes. Rather than rest on our laurels with our questionnaire, we continuously adapt based on the user’s input to the system.
We use Machine Learning to help our users find their most compatible match, starting with baseline inputs based on psychology and statistics and continue to learn based on their swipe history. Our algorithm will improve the more you swipe and provide real time updates to your swipe queue as we continuously learn.
So, to feed our process, we needed to define the inputs. After much development, experimentation and testing, we've collected inputs to feed each thing we learn.
Here’s a short list of what we will learn as you use our app:
1. We learn physical attraction.
All of our users’ photos are analyzed for key indicators that help us understand what attracts you to them. Input varies from skin and eye color to ratios of facial points. We’ll take whatever we can find from the submitted photos and use the available data as inputs.
But as the famous saying goes “Beauty is in the eye of the beholder”.
There’s some argument over whether the Golden Ratio works for attraction on faces and, while we consider a baseline for attraction, we by no means define it for you. You do the swiping and we can discover what you find attractive.
2. We learn compatibility.
Being a swipe app, physical attraction will always largely dominate the actions of a user. However, we feel it necessary to ensure that our users can also see how compatible they are with the candidate matches, providing a baseline questionnaire that we ask them to fill out when signing up and then learning the relationships between their compatible attributes with those they will swipe against.
Swiping is generally a fast action, but taking it slower and reading up about your potential match can help us acknowledge your efforts and tailor your experience based on those inputs. We don't ask hundreds of questions. We ask a dozen of you and learn the relationships ourselves.
3. We learn what’s most important to you.
Swiping patterns are different for all users: While some people will swipe on any pretty face, others will take more time to process what they have in common with the user. Various patterns of swiping must be recognized for any system such as ours to work.
But this is where we might teach you a thing or two about yourself. Perhaps you are physically attracted to a specific face and body, but will still swipe left if that person, say, doesn't like cats and dogs. Perhaps you prefer to date people who share your same political views or religion but that special look of somebody will make you ignore all that. Or perhaps physical attraction is not important to you at all and you just want to find somebody who loves all the same things that you do.
We don't tell you that you should find either attraction or compatible traits more important.
We let you tell us.
There are some companies using collaborative filtering to fulfill their promise of a more intelligent dating application. It's not a terrible approach – one where you are shown certain profiles because other people with similar swipe patterns also liked them (think the Amazon.com "Customers also bought this..") – but that is not what we at Say Allo are doing. We don't want to learn about anybody who might be like you. We want to learn about you.
So, please give us a shot, help us build up our user base and we'll help you find that special someone so that you'll make us the last dating app you'll ever need!