Dating website optimizer Free online girls video chatting without registration
On dating apps, men & women who have a competitive advantage in photos & texting skills will reap the highest ROI from the app.
As a result, I’ve broken down the reward system from dating apps down to a formula, assuming we normalize message quality from a 0 to 1 scale: The better photos/good looking you are you have, the less you need to write a quality message.
Once it finished learning what I like, the DATE-A MINER will automatically swipe left or right on each profile on my Tinder application.
As a result, this will significantly increase swipe volume, therefore, increasing my projected Tinder ROI.
Then I scraped these images and used them within my dataset. The Classifier, essentially uses multiple positive/negative rectangles.
Now that I have the images, there are a number of problems. Passes it through a pre-trained Ada Boost model to detect the likely facial dimensions: The Algorithm failed to detect the faces for about 70% of the data. To model this data, I used a Convolutional Neural Network.
While this doesn’t give me a competitive advantage in photos, this does give me an advantage in swipe volume & initial message.
Because my classification problem was extremely detailed & subjective, I needed an algorithm that could extract a large enough amount of features to detect a difference between the profiles I liked and disliked.
A c NN was also built for image classification problems.
I just think that the mindless swiping is a waste of my time and prefer to meet people in person.
However, the problem with this, is that this strategy severely limits the range of people that I could date.
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If you have bad photos, it doesn’t matter how good your message is, nobody will respond.