> - the KPI "do-more-users-leave-our-platform-earlier-if-our-matching-algo-is-just-too-good" - I promise: In alle the years, this question WAS NEVER - NEVER!!!!!!! - raised, regardless wich Manager or which Exec. This metric isnt even debated.
What labels do they use for training their algos though? What is their definition of a successful match, is it a date, a recurring date, or something closer to a long-term relationship?
If matches predominantly result in "failure" they might just not have enough "long-term success" labels to go by, and their proxy labels will be biased towards short-term successes.
Wrong approach;
at least until latest, NONE of the standard apps does apply any kind of "real AI" stuff, maybe this changed through the last 3 - 4 years.
All thi matching stuff like "match with X%" is just bullshit.
The only platform having a useful approach here was OKC years ago. (but even for their scoring you would not need any type of sophisticated tech)
What labels do they use for training their algos though? What is their definition of a successful match, is it a date, a recurring date, or something closer to a long-term relationship?
If matches predominantly result in "failure" they might just not have enough "long-term success" labels to go by, and their proxy labels will be biased towards short-term successes.