> From the company's POV, aren't they likely to benefit from setting you up on dates that either go mildly unfortunately, or leading to a short-term relationship at best? The customers are paying for dates after all, not long term relationships.

Short-term, probably yeah. But long-term, if they want to get a reputation like what parent describes ("I met my wife on platform X") then they better optimize for good dates rather than "unfortunate" ones.

Most businesses only look at growth, and for this they only require short to medium term satisfaction of their users. Long term satisfaction, such as marriage, is mostly irrelevant.

There is something funny about the financial incentives of a dating app pushing the app to make sure its users don't date.

I mean they're dating apps, not relationship apps! Tinder was built to facilitate quick hookups, not being selective about your next long-term relationship.

Of course, Tinder is synonymous to dating for some, which is the weird part.

Notably, I don’t think an app helping manage a relationship would gain traction. Which would seem to be the extension of your pointing out they’re dating apps.

>From the company's POV, aren't they likely to benefit from setting you up on dates that either go mildly unfortunately, or leading to a short-term relationship at best? The customers are paying for dates after all, not long term relationships.

I can't imagine there's a data scientist alive who could devise an algorithm that accurately, reliably predicts "kind of a good match but not really", given only some limited inputs (basic info + photos).

I reckon from the company's perspective that positive word of mouth from people who like the app is a much better focus point.

I was more thinking based on input data about things like what you are looking for in a partner, whether you are after a long-term relationship or not, etc. Then you throw in all that data to the ML deities and hope the output is something roughly related to what you're after.

Fair enough about word of mouth though, relationship durations are probably linearly correlated to the number of endorsements given by each customer - at some point in a relationship you end up with a rehearsed "so how did you two meet?"!

Actually I think this would be relatively easy, you're just looking for a bit of alignment in worldview and significant desperation.

The old ok cupid had a bunch of quiz questions that it'd match you for, both similarity and dissimilarity weighted by self reported importance.

Then Match Group bought them out and mostly gutted that system and turned it into another swiper :/

There's still eharmony, but I think that's just for white Christian boomers mainly.

The "limited" inputs include: All your conduct on the app, your "advertising" dossier.

Seems doable, frankly.

> I can't imagine there's a data scientist alive who could devise an algorithm that accurately, reliably predicts "kind of a good match but not really", given only some limited inputs (basic info + photos).

One approach would be to make the problem easier, just preferentially match people with a partner who is known to have short relationships and no/few awful reviews, based on history on the site.