The Engine:
Vector Interest Profiles
No raw spying. No black box. Just transparent, decaying scores that evolve with you.
User ≠ Label. User = Evolving Vector.
1. The Principle: You Are A Score, Not A Label
Most platforms tag you as "Student" and leave you there. We allow you to be complex. Your profile is made of confidence scores that rise and fall.
- Explainable
"You see this because your hiking score is 0.82."
- Decaying
If you stop hiking, your hiking score drops naturally.
2. The Three Signal Layers
Where do these scores come from? We use 3 explicit sources.
Cold Start (Onboarding)
Explicit data users give us when they join. It sets the baseline but fades over time.
- Selected City
- Budget Range
- Chosen Interests
Behavioral (The Truth)
What users actually do. This is the strongest signal and naturally overtakes the initial profile.
- Dwell Time (18s+)
- Communities Joined
- Events Saved
Social Connections
Optional external signals converted into abstract scores (never raw data).
- LinkedIn (Career)
- .edu Email (Student)
- Friend Circles
3. Ethical Scoring Rules
You are in control. The system cleans itself automatically so you never get stuck in an old bubble.
Transparency
"You were added to 'Lisbon Surfers' because you viewed 5 surf events."
Natural Decay (The Anti-Bubble)
Old interests fade. If you stop clicking 'Party', your profile shifts to 'Chill'.
Live Decay Simulation
4. Gravity Auto-Join System
We don't ask you search. We place you where you belong.
The Formula
If the score is high enough, you are instantly added to the hub.
Max 3-5 core hubs to prevent overwhelm.
Smart Suggestions
For everything else, we suggest rather than force.
5. One Brain. Many Outputs.
Your Interest Vector drives the entire platform experience.
6. Predictive Recommendation Engine
Our original powerhouse. It scans explicit signals (filters, searches) and contextual data (location, budget) to proactively find the perfect housing, visa services, and local perks.
- Intent-Based Search Ranking
- Hyper-Local Service Matching
- Real-Time Inventory (APIs)
7. The Intelligence Pipeline
How meaningful signals become magical experiences.
Signal Ingestion
The system focuses on implicit data: Time spent on a card (Dwell Time), Community Hub joins, Job saves, and search filters. This builds a "Context Vector" without the user needing to type a single word.
Semantic Intent
The NLP Cortex uses Semantic Embeddings (Hugging Face) to understand concepts. It knows that reading about "Blue Card" means "Visa" and "Relocation", connecting dots across different categories.
Community Graph
The engine looks at your Community Hub memberships. If you join "Digital Nomads in Bali", it filters housing for "Laptop Friendly" and "High-speed WiFi" automatically.
Predictive Ranking
Finally, the LambdaMART Ranker scores every item based on probability. It promotes the hidden gem that perfectly matches your budget and your friend's recent activity.
8. Core Algorithms & Tech Stack
The mathematical engine running underneath.