GC General Chat
Hier kun je praten over van alles en nog wat. Eigenlijk alles wat niet in een van de andere rubrieken thuishoort.
Bijzonderquote:Op zaterdag 28 september 2024 22:57 schreef datismijnmening het volgende:
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Nee gare klassieke muziek met toeters en Bicycle van Queen
Ik word echt overspoeld met profielen op eens. Het algoritme vindt het schijnbaar leuk als je een match hebt.
100.000 katjes
Fuck the EBU!
Fuck the EBU!
Net zoals vrouwen dat vinden. Dan ben je ineens populair.quote:Op zaterdag 28 september 2024 23:06 schreef Nattekat het volgende:
Ik word echt overspoeld met profielen op eens. Het algoritme vindt het schijnbaar leuk als je een match hebt.
Slim algoritme dan.quote:
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Net zoals vrouwen dat vinden. Dan ben je ineens populair.
100.000 katjes
Fuck the EBU!
Fuck the EBU!
Morgenquote:Op zaterdag 28 september 2024 23:08 schreef Citizen.Erased het volgende:
Wanneer ga je daten dan Nattekat?
100.000 katjes
Fuck the EBU!
Fuck the EBU!
Je krijgt wel voorkeur als je al eens een date hebt gehad en niet sjaak afhaak bent.quote:
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Veel apps begonnen met ELO-score. Nu noemen ze het vast iets hips met AI.
100.000 katjes
Fuck the EBU!
Fuck the EBU!
Door de regen fietsen omdat dat mijn geluk de afgelopen weken is geweest.quote:
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Wat ga je doen dan? Niet door de regen fietsen vantevoren .
Gewoon een drankje
100.000 katjes
Fuck the EBU!
Fuck the EBU!
Veel plezierquote:
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Door de regen fietsen omdat dat mijn geluk de afgelopen weken is geweest.
Gewoon een drankje
Nog een gratis tip, laat de vanillevla thuis, bestijg haar als een wild beest, trek aan haar haren en laat de ware koning in jezelf ontwaken.quote:
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Door de regen fietsen omdat dat mijn geluk de afgelopen weken is geweest.
Gewoon een drankje
Baby got back - Sir Mixalotquote:
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Misschien moet ik mijn playlist nog even updaten voordat mensen ook muziek met toeters uitzoeken.
-XOXO-
Wat is ELO?quote:
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Veel apps begonnen met ELO-score. Nu noemen ze het vast iets hips met AI.
-XOXO-
Speltip 8: doe het tegenovergestelde van wat US zegt
Nog altijd relevant
Nog altijd relevant
100.000 katjes
Fuck the EBU!
Fuck the EBU!
quote:
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Dat werkt zo ongeveer als ratingsysteem bij schaken.
-XOXO-
In a dating app like Tinder, the ELO rating system (or something similar) could be used to determine the "attractiveness" or popularity of users based on how often they get swiped right (liked) or swiped left (rejected). Here's a simplified version of how it could work:quote:
Starting Score:
Every user starts with a base score, let’s say 1000, which represents their initial attractiveness or popularity score.
Interactions (Swipes):
When you swipe right on someone (like them), or someone swipes right on you, the app evaluates the relative "scores" of both people involved:
You get swiped right: If someone with a higher score swipes right on you (likes you), your score increases more than if someone with a lower score swipes right.
You swipe right on someone: If you swipe right on a highly rated person and they swipe back, your score could increase more than if you matched with someone who has a lower score.
Swipe Left: When someone swipes left (rejects you), your score might decrease slightly, especially if the person rejecting you has a much higher score. Similarly, if you swipe left on someone, it can slightly affect their score depending on your rating.
Matching: Matches (mutual right swipes) between users with close scores would suggest the system has made an accurate pairing. But when someone with a much higher score matches with someone with a lower score, the lower-rated person would gain more points, as it's considered a more “valuable” match.
Point Exchange:
If you consistently get swiped right by highly rated users, your score increases rapidly.
If higher-rated users frequently swipe left on you, your score could drop.
If you get swiped right by lower-rated users, it might increase your score a little or have no significant effect, depending on how big the difference is.
Example:
User A has a score of 800, and User B has a score of 1200.
If User B swipes right on User A (an unexpected outcome), User A's score might jump by 30 points, going up to 830, while User B’s score might decrease slightly or stay stable.
If User A swipes right on User B (an expected outcome), User B’s score might increase a little, but User A’s score might not change much unless it results in a mutual match.
The Result:
Over time, users with more mutual likes, especially from high-rated users, will have a higher score. The system could use this score to show more “popular” users to each other, while users with lower scores might see others with similar ratings. This keeps the user experience balanced and tailored to preferences based on past interactions.
In short, the system tries to match people based on how desirable or "liked" they are, making popular users more likely to see and be seen by other popular users.
quote:
Ik word echt overspoeld met profielen op eens. Het algoritme vindt het schijnbaar leuk als je een match hebt.
quote:
mentions en alerts staan uit, pm/dm mij
Op het einde van de dag trouwt Lana Del Rey met iemand die alligator tours geeft, dus misschien is het niet helemaal waterdicht.
daten klinkt maar ingewikkeld , laat ik daar maar nooit aan beginnenquote:Op zaterdag 28 september 2024 23:41 schreef Citizen.Erased het volgende:
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In a dating app like Tinder, the ELO rating system (or something similar) could be used to determine the "attractiveness" or popularity of users based on how often they get swiped right (liked) or swiped left (rejected). Here's a simplified version of how it could work:
Starting Score:
Every user starts with a base score, let’s say 1000, which represents their initial attractiveness or popularity score.
Interactions (Swipes):
When you swipe right on someone (like them), or someone swipes right on you, the app evaluates the relative "scores" of both people involved:
You get swiped right: If someone with a higher score swipes right on you (likes you), your score increases more than if someone with a lower score swipes right.
You swipe right on someone: If you swipe right on a highly rated person and they swipe back, your score could increase more than if you matched with someone who has a lower score.
Swipe Left: When someone swipes left (rejects you), your score might decrease slightly, especially if the person rejecting you has a much higher score. Similarly, if you swipe left on someone, it can slightly affect their score depending on your rating.
Matching: Matches (mutual right swipes) between users with close scores would suggest the system has made an accurate pairing. But when someone with a much higher score matches with someone with a lower score, the lower-rated person would gain more points, as it's considered a more “valuable” match.
Point Exchange:
If you consistently get swiped right by highly rated users, your score increases rapidly.
If higher-rated users frequently swipe left on you, your score could drop.
If you get swiped right by lower-rated users, it might increase your score a little or have no significant effect, depending on how big the difference is.
Example:
User A has a score of 800, and User B has a score of 1200.
If User B swipes right on User A (an unexpected outcome), User A's score might jump by 30 points, going up to 830, while User B’s score might decrease slightly or stay stable.
If User A swipes right on User B (an expected outcome), User B’s score might increase a little, but User A’s score might not change much unless it results in a mutual match.
The Result:
Over time, users with more mutual likes, especially from high-rated users, will have a higher score. The system could use this score to show more “popular” users to each other, while users with lower scores might see others with similar ratings. This keeps the user experience balanced and tailored to preferences based on past interactions.
In short, the system tries to match people based on how desirable or "liked" they are, making popular users more likely to see and be seen by other popular users.
mentions en alerts staan uit, pm/dm mij
Oftewel leeuwen aan hertjes koppelen en blobvissen aan varkentjes.quote:
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In a dating app like Tinder, the ELO rating system (or something similar) could be used to determine the "attractiveness" or popularity of users based on how often they get swiped right (liked) or swiped left (rejected). Here's a simplified version of how it could work:
Starting Score:
Every user starts with a base score, let’s say 1000, which represents their initial attractiveness or popularity score.
Interactions (Swipes):
When you swipe right on someone (like them), or someone swipes right on you, the app evaluates the relative "scores" of both people involved:
You get swiped right: If someone with a higher score swipes right on you (likes you), your score increases more than if someone with a lower score swipes right.
You swipe right on someone: If you swipe right on a highly rated person and they swipe back, your score could increase more than if you matched with someone who has a lower score.
Swipe Left: When someone swipes left (rejects you), your score might decrease slightly, especially if the person rejecting you has a much higher score. Similarly, if you swipe left on someone, it can slightly affect their score depending on your rating.
Matching: Matches (mutual right swipes) between users with close scores would suggest the system has made an accurate pairing. But when someone with a much higher score matches with someone with a lower score, the lower-rated person would gain more points, as it's considered a more “valuable” match.
Point Exchange:
If you consistently get swiped right by highly rated users, your score increases rapidly.
If higher-rated users frequently swipe left on you, your score could drop.
If you get swiped right by lower-rated users, it might increase your score a little or have no significant effect, depending on how big the difference is.
Example:
User A has a score of 800, and User B has a score of 1200.
If User B swipes right on User A (an unexpected outcome), User A's score might jump by 30 points, going up to 830, while User B’s score might decrease slightly or stay stable.
If User A swipes right on User B (an expected outcome), User B’s score might increase a little, but User A’s score might not change much unless it results in a mutual match.
The Result:
Over time, users with more mutual likes, especially from high-rated users, will have a higher score. The system could use this score to show more “popular” users to each other, while users with lower scores might see others with similar ratings. This keeps the user experience balanced and tailored to preferences based on past interactions.
In short, the system tries to match people based on how desirable or "liked" they are, making popular users more likely to see and be seen by other popular users.
Dat is wel de samenvatting. Iedereen gelukkig op het eind.quote:
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Oftewel leeuwen aan hertjes koppelen en blobvissen aan varkentjes.
Als je alleen maar varkentjes te zien krijgt dan weet je eigenlijk ook wel genoeg.quote:
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Dat is wel de samenvatting. Iedereen gelukkig op het eind.
