AI Volunteer Matching System

AI-based volunteer matching system

Volunteer management platforms often face a mismatch problem: dozens of unfilled positions alongside unengaged volunteers. AI matching finds optimal matches based on skills, availability, location, and preferences, increasing the fill rate by 40-60%.

Smart Matching Volunteer - Position

import pandas as pd
import numpy as np
from anthropic import Anthropic

def match_volunteers_to_positions(volunteers: pd.DataFrame,
                                   positions: pd.DataFrame,
                                   top_k: int = 3) -> list[dict]:
    """
    Двусторонний матчинг: находим лучших кандидатов для каждой позиции.
    volunteers: id, skills[], availability_days[], location, experience_years, languages[]
    positions: id, required_skills[], date, location, min_experience, languages_needed[]
    """
    matches = []

    for _, position in positions.iterrows():
        scored = []

        for _, volunteer in volunteers.iterrows():
            # Навыки
            vol_skills = set(volunteer.get('skills', []))
            req_skills = set(position.get('required_skills', []))
            skill_match = len(vol_skills & req_skills) / max(len(req_skills), 1)

            if skill_match == 0:
                continue  # Нет обязательных навыков — пропускаем

            # Доступность
            pos_date = str(position.get('date', ''))
            available = pos_date in volunteer.get('availability_days', []) or not pos_date
            if not available:
                continue

            # Локация (расстояние или совпадение города)
            location_match = int(volunteer.get('location') == position.get('location'))

            # Язык
            pos_lang = set(position.get('languages_needed', []))
            vol_lang = set(volunteer.get('languages', ['ru']))
            lang_match = int(bool(pos_lang.issubset(vol_lang)) or not pos_lang)

            # Опыт
            min_exp = position.get('min_experience_years', 0)
            exp_match = min(1.0, volunteer.get('experience_years', 0) / max(min_exp, 1))

            score = (
                skill_match * 0.45 +
                location_match * 0.25 +
                lang_match * 0.15 +
                exp_match * 0.15
            )

            scored.append({
                'volunteer_id': volunteer['id'],
                'position_id': position['id'],
                'score': round(score, 3),
                'skill_coverage': round(skill_match, 2)
            })

        top = sorted(scored, key=lambda x: -x['score'])[:top_k]
        matches.extend(top)

    return matches

AI-based volunteer matching reduces the time it takes to fill positions from 5-7 days to 1-2 days and increases volunteer retention: when a person is matched to a suitable role, the likelihood of repeat participation increases by 35-45%.