밀프레드 편식도감
스키마 · UX · 알고리즘

profile = {
    'ingredient_stats': {
        '시금치': {
            'exposure_count': 22,
            'eat_count': 8,
            'accept_rate': 0.36,
            'last_seen_days_ago': 3,
            'preferred_forms': ['즙', '다진'],
            'rejected_forms': ['통째']
        }, ...
    },
    'base_stats': {
        '계란찜': {'eat_count': 43, 'preference_score': 0.95}, ...
    },
    'texture_distribution': {'죽': 0.65, '매시': 0.25, ...},
    'nutrient_status': {'iron': 'red', 'choline': 'red', ...}
}

WITH preferred_bases AS (
  SELECT r.base_type, SUM(CASE WHEN ml.reaction IN ('잘먹음','또달라') THEN 1 ELSE 0) AS cnt
  FROM meal_logs ml JOIN meal_log_ingredients mli ON ml.id = mli.meal_log_id
                  JOIN recipes r ON (...메뉴명 매칭...)
  WHERE ml.child_id = $1 AND ml.eaten_at > NOW() - INTERVAL '30 days'
  GROUP BY r.base_type ORDER BY cnt DESC LIMIT 5
),
challenge_ings AS (
  SELECT id FROM ingredients
  WHERE id IN (SELECT ing_id FROM low_accept_view WHERE child_id = $1)
  LIMIT 10
)
SELECT r.*,
  base_score(r.base_type) * concealment_score(r.id) AS score
FROM recipes r
WHERE r.base_type IN (SELECT base_type FROM preferred_bases)
  AND EXISTS (
    SELECT 1 FROM recipe_ingredients ri
    WHERE ri.recipe_id = r.id
      AND ri.ingredient_id IN (SELECT id FROM challenge_ings)
      AND ri.form IN ('즙','다진','매시')
  )
ORDER BY score DESC LIMIT 50;

1. 대상 식재료: accept_rate ≥ 0.7 AND last_seen_days_ago > 30 AND 모든 preferred_forms이 가린 형태('즙','다진','매시')
2. 레시피 필터: 해당 재료를 통째·스틱·큐브 형태로 사용하는 레시피
3. texture_level > 현재 사용자 최대 texture (점진 단계 ↑)
4. 랭킹: age_appropriateness(texture, age_months) × loved_score(ing)

1. 대상: accept_rate < 0.3 AND exposure_count ≥ 5
2. 레시피 필터: concealment[ing] == 'high' (즙·페이스트)
3. 추가: base_type ∈ preferred_bases (베이스도 친숙)
4. 랭킹: concealment_score × base_preference_score

WITH red_nutrients AS (
  SELECT nutrient FROM signal_36_view
  WHERE child_id = $1 AND status = 'red'
  ORDER BY pct ASC LIMIT 5
)
SELECT r.*, (
  SELECT SUM(
    (r.nutri_total->>n.nutrient)::DECIMAL /
    (SELECT rni FROM kdri_rni WHERE age_band = $2 AND nutrient_code = n.nutrient)
  )
  FROM red_nutrients n
) AS boost_score
FROM recipes r
WHERE r.nutrient_highlights && ARRAY(SELECT nutrient FROM red_nutrients)
ORDER BY boost_score DESC LIMIT 80;

score = (
    base_pref_weight * base_preference_score(recipe.base_type, profile.base_stats) +
    concealment_weight * concealment_score(recipe, challenge_ings) +
    nutri_weight * nutrient_boost_score(recipe, red_nutrients) +
    novelty_weight * (1 if recipe not in recent_30days else 0) +
    age_fit_weight * age_appropriateness(recipe.age_group, child.age_months)
)
# 기본 가중치 (튜닝 가능)
DEFAULTS = {'base_pref':0.3, 'concealment':0.2, 'nutri':0.3, 'novelty':0.1, 'age_fit':0.1}

Phase 1 결과 190 후보 → Claude에 전달 → 원칙별 최적 + 자연어 이유 생성.

def refine_with_llm(profile, candidates_190):
    system = """당신은 만 0-5세 영유아 식습관 데이터를 분석해..."""
    user = f"""
## 프로파일
{profile_to_natural_language(profile)}

## 190 후보 (Phase 1)
{json.dumps(candidates_190, ensure_ascii=False)}

## 출력 형식 (JSON Schema)
{
  "principle_1": [{"recipe_id": "uuid", "reason": "3-4문장 자연어"}, ...],
  "principle_2": [...], "principle_3": [...], "principle_4": [...]
}
"""
    response = anthropic.messages.create(
        model="claude-sonnet-4-7",
        max_tokens=4000,
        system=[{"type":"text","text":system,"cache_control":{"type":"ephemeral"}}],
        messages=[{"role":"user","content":user}]
    )
    return parse_and_validate(response.content)

def compute_profile_hash(profile):
    # 변동성 낮은 핵심만 해싱 → 캐시 hit rate ↑
    key_fields = {
        'top5_bases': sorted(top_5(profile.base_stats))[:5],
        'red_nutrients': sorted(profile.red_nutrients)[:5],
        'challenge_ings': sorted(top_10_challenge(profile))[:10],
        'age_band': profile.age_band,
    }
    return sha256(json.dumps(key_fields, sort_keys=True)).hexdigest()