vyouth.kw | Instagram, TikTok | Linktree

The AI Assessment System combines temporal data modeling with Large Language Models (LLMs) to provide context-aware scheduling recommendations.

Temporal patterns of volunteer availability are represented as time-series vectors, analyzed using K-Means Time Clustering and Decision-Tree Mapping to align free-time windows with event schedules.

LLMs enhance this process by interpreting natural-language inputs (e.g., “I’m free on Friday evenings”) and converting them into structured time features.
They also perform contextual reasoning, linking user preferences and event descriptions beyond raw time data.

The result is a hybrid intelligent system that learns when and what to recommend, making volunteering coordination adaptive and conversational.

AI Assessment System

ليش ما تطوعت؟ | Coming Soon

Deep Technical AI Explanation

Social Media

Our engagement prediction module is built using a Supervised Machine Learning approach.
Currently, we are experimenting with both Logistic Regression and a Random Forest Classifier to identify which performs better in predicting volunteer activity patterns.

The model is trained on behavioral features such as participation intervals, event completion rates, and sentiment extracted from volunteer feedback.
It outputs a binary classification: active or at-risk.

When the system predicts that a volunteer is at-risk, it automatically triggers a context-aware notification, designed to re-engage the user and encourage continued participation.

Engagement Prediction Model

The matching process in VYouth is powered by a Content-Based Recommendation Engine combined with Cosine Similarity to measure how closely a volunteer’s profile aligns with a volunteering opportunity.

The algorithm extracts and vectorizes key features from both sides — including activity categories, skill sets, and historical engagement — to generate a similarity score for each potential match.

In future iterations, we aim to extend the model with Collaborative Filtering through Matrix Factorization, enabling the system to learn cross-user behavioral patterns and provide more adaptive and community-driven recommendations.

AI Matching System

The ranking module employs a weighted scoring algorithm designed to balance both quantitative activity and qualitative performance.

Each volunteer’s rank is computed using the following formula:
Rank = (0.5 × Number of Events) + (0.3 × Average Rating) + (0.2 × Engagement Consistency Score)

This approach ensures an equitable evaluation by incorporating multiple behavioral dimensions, preventing bias toward users who simply participate frequently without maintaining consistent or high-quality engagement.

Ranking System

تبي تسجل معانا؟

Snapchat

TikTok

Website

Instagram

@vyouth.kw

Enjoy 30 days of Hulu for FREE

Claim 10 free meals* from HelloFresh

Get 60% off Sitewide

Sip your way to stronger health with 30% off at ARMRA

Save 10% at Omnilux LED

Save 10% on Ritual Multivitamins Subscriptions

You've scored 50% OFF Factor 🤤

Join now and get 50% off a Club membership

Get your first box FREE with Curology!

Claim your $20 bonus with Acorns

Sign up for Headspace Today and get a 14 day free trial!

Get AAA membership for half the price!

Get 30¢/gal cash back on gas at major stations

Get 15% off at Equip Foods

20% Off the at FabFitFun with code INSIDER20

Get 80% off your first order when signing up to be a VIP!

Get 15% off for cleaner skin at CLEARSTEM

Get your first box FREE with Agency!

50% off + Free Shipping at Purple Carrot!

Power Up with Daily Harvest's High-Protein Smoothies

Save $100 with Gobble

Save 10% on your First Maev Order!

15% off your favorite non-alcoholic spirits at The Zero Proof

10% off JLab’s award‑winning audio gear

Get help with or without insurance. Use code SPACE80 for $80 off.

Start speaking a new language in just 3 weeks