Answer what matters
Your profile, selected interests, priorities, and compatibility answers give Likert more signal than a swipe.
How Likert makes dating discovery smarter.
Likert keeps browsing familiar, then uses compatibility answers, ratings, skips, and feedback to make For You more relevant over time while exact interest stays private.
The product loop: answer, react, discover, match.
This is how the learning loop works: keep browsing naturally, give Likert richer answers and feedback, and let For You improve who appears first.
React privately
Ratings, skips, and feedback teach the algorithm what feels right without turning reactions into public labels.
See smarter recommendations
For You uses those signals to adjust discovery, so more relevant people can move up over time.
Match with context
When interest is mutual, compatibility helps frame why the conversation may be worth starting.
How Likert learns what feels relevant.
Likert combines what you say, what you select, and how you privately respond to make For You feel more relevant over time. Exact interest and private compatibility answers are not public votes, public rankings, or attractiveness labels.
Profile preferencesCompatibility answersSelected interestsRatings, skips, and feedback
Smarter For You orderingRecommendations that improve over time
The product proof is in For You.
The For You screen keeps browsing familiar while learning from what you answer, rate, skip, and respond to. Likert separates discovery, learning, and matching so richer private answers and feedback can improve recommendations without becoming public scorekeeping.
Profile context
Profiles carry preferences, priorities, interests, and compatibility answers into discovery.
Private feedback
Ratings, skips, and exact interest teach For You what feels relevant without becoming public.
Recommendation order
Likert can move stronger fits higher as it learns from answers and reactions.
Compatibility preview
Private answers help explain why a profile may be worth attention.
Personal rating
Your reaction teaches Likert without publishing a score.
Profile context
Readable profiles stay simple while deeper signals work behind the scenes.
- Compatibility previewPrivate answers help explain why a profile may be worth attention.
- Personal ratingYour reaction teaches Likert without publishing a score.
- Profile contextReadable profiles stay simple while deeper signals work behind the scenes.
The old model was swiping. The new model learns.
Swipe apps reset after every profile. Likert keeps dating familiar, but turns answers and private feedback into better recommendations as you use it.
Swipe loop
Fast, familiar, and often repetitive.
- See a face
- Swipe fast
- Repeat again
- Hope it means something
Likert loop
Familiar browsing with a learning layer underneath.
- Answer what matters
- Rate privately
- For You learns
- See better fits
When interest is mutual, matching starts with more context.
Private answers and ratings stay private. They help Likert order For You without becoming public votes, public rankings, or attractiveness labels.
What Likert gives the matching model that swipes do not.
Richer signals
Profile details, priorities, interests, and 200+ compatibility signals give the algorithm more context than a swipe.
Learning discovery
For You learns from what you rate, skip, and respond to, so the order can get sharper as you use it.
Built around better recommendations
Likert opens carefully so members see enough nearby people and get recommendations shaped by fit, not endless motion.
Personal answers, not public scorekeeping.
Useful data, not public clutter
Likert asks for information that can make matching better, then keeps deeper answers out of public profile presentation.
Controls people can understand
Privacy, visibility, notification, analytics, subscription, and deletion choices should be easy to find.
Retention is plain
The policy explains deletion timing, recovery windows, safety exceptions, legal needs, billing, and support record handling.
