I’m currently going through a painful divorce so of course I’m starting to look into dating apps as a superficial coping mechanism.
It seems to me that even the modern dating apps like Tinder and Bumble could be made a lot better with a tiny bit of machine learning. After a couple thousand swipes (which doesn’t take long), I would think that a machine learning system could get a pretty good sense of my tastes and perhaps some metric of my minimum standards of attractiveness. This is particularly true for a system that has access to all the swiping data across the whole platform.
Since I swipe completely based on superficial appearance without ever reading the bio (like most people), the system wouldn’t need to take the biographical information into account, though I suppose it could use that information as well.
The ideal system would quickly learn my preferences in both appearance and personal information and then automatically match me up with the top likely candidates. I know these apps keep track of the response rates of individuals, so matches who tend not to respond often (probably due to being very generally desirable) would be penalized in your personal matchup ranking—again, something machine learning could handle easily.
I find myself wondering why this doesn’t already exist.
There aren’t that many people, so the benefits would be minor. Once you’ve swiped a couple thousand times you’re probably through most of the tinder users within your demographic preferences.
I think it’s highly likely that an App like Tinder doesn’t do the matching completely random but optimizes for some factor.
Your analysis ignores the fact that Tinder principle is about woman only getting messages from guys on whom they previously swipped left and thus signaled that they want to receive messages from the guy. That ritual has psychological value.
If you do want a more explicit recommendation system, sites like eharmony can provide for that need.
I considered creating something like that to be used with Tinder’s (unofficial) API. There are a bunch of freely available algorithms one might use for this purpose. I did not seriously attempt this because it’s a hard problem, the algorithms are unreliable and difficult, and I’m not even sure if it’s something I want or could profit from.
As for why Tinder hasn’t done this. It goes against their business model. They would make less money. Tinder wants to keep you as an user for as long as possible, and the whole process of swiping, always wondering what the next one will be like, is their most addictive feature. Ideally they’ll only let you go on dates if it’s really necessary to keep you as a user. I’d guess that a significant portion of their users just use the app for swiping.
I’m currently going through a painful divorce so of course I’m starting to look into dating apps as a superficial coping mechanism.
It seems to me that even the modern dating apps like Tinder and Bumble could be made a lot better with a tiny bit of machine learning. After a couple thousand swipes (which doesn’t take long), I would think that a machine learning system could get a pretty good sense of my tastes and perhaps some metric of my minimum standards of attractiveness. This is particularly true for a system that has access to all the swiping data across the whole platform.
Since I swipe completely based on superficial appearance without ever reading the bio (like most people), the system wouldn’t need to take the biographical information into account, though I suppose it could use that information as well.
The ideal system would quickly learn my preferences in both appearance and personal information and then automatically match me up with the top likely candidates. I know these apps keep track of the response rates of individuals, so matches who tend not to respond often (probably due to being very generally desirable) would be penalized in your personal matchup ranking—again, something machine learning could handle easily.
I find myself wondering why this doesn’t already exist.
There aren’t that many people, so the benefits would be minor. Once you’ve swiped a couple thousand times you’re probably through most of the tinder users within your demographic preferences.
The situation changes if you regularly travel, though.
“Once” does exactly what you have described.
Or maybe some kind of recommendation system: “Users who dated this person also dated these: …”
Yeah… that might be interesting for Tinder. “Users who fucked this one also fucked this, this, and that one” X-D
Why do you think this doesn’t exist?
I think it’s highly likely that an App like Tinder doesn’t do the matching completely random but optimizes for some factor.
Your analysis ignores the fact that Tinder principle is about woman only getting messages from guys on whom they previously swipped left and thus signaled that they want to receive messages from the guy. That ritual has psychological value.
If you do want a more explicit recommendation system, sites like eharmony can provide for that need.
I considered creating something like that to be used with Tinder’s (unofficial) API. There are a bunch of freely available algorithms one might use for this purpose. I did not seriously attempt this because it’s a hard problem, the algorithms are unreliable and difficult, and I’m not even sure if it’s something I want or could profit from.
As for why Tinder hasn’t done this. It goes against their business model. They would make less money. Tinder wants to keep you as an user for as long as possible, and the whole process of swiping, always wondering what the next one will be like, is their most addictive feature. Ideally they’ll only let you go on dates if it’s really necessary to keep you as a user. I’d guess that a significant portion of their users just use the app for swiping.