refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-dynamic-creative-optimization
+title: Dynamic Creative Optimization (DCO)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [DCO, dynamic ad, programmatic creative, ad personalization, creative AI]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.94
+verification_status: applied
+tags: [advertising, ad-tech, dco, personalization, generative-ai, mab, optimization]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python / TypeScript
+  framework: AWS / GCP / DSP
+---
+
+# Dynamic Creative Optimization (DCO)
+
+## 매 한 줄
+> **"매 ad creative 의 user / context 의 real-time 의 assemble"**. 매 static ad → 매 millions of variant. 매 element-level (image + headline + CTA + offer). 매 modern: 매 LLM + diffusion 의 generate, 매 RL bandit 의 select.
+
+## 매 핵심
+
+### 매 motivation
+- **Static ad**: 매 single creative.
+- **DCO**: 매 user 의 context 의 best variant.
+- **Result**: 매 CTR 30-200% ↑ (typical).
+
+### 매 element
+- **Hero image / video**: 매 product, lifestyle.
+- **Headline**: 매 hook.
+- **Body**: 매 detail.
+- **CTA**: "Buy", "Learn", "Sign Up".
+- **Offer**: 매 discount, urgency.
+- **Logo / brand color**.
+
+### 매 method
+- **Rules-based**: 매 segment → variant.
+- **MAB / contextual bandit**: 매 explore-exploit.
+- **RL**: 매 long-term reward.
+- **Generative**: 매 LLM headline + diffusion image.
+- **Predictive CTR**: 매 model 의 score.
+
+### 매 modern AI
+- **Generative DCO**: 매 millions of unique creative.
+- **Persona-based**: 매 LLM 의 segment 의 voice.
+- **A/B at scale**: 매 thousands variant.
+- **Brand safety**: 매 LLM filter.
+
+### 매 응용
+1. **E-commerce**: 매 product feed-driven.
+2. **Travel**: 매 destination + season.
+3. **Finance**: 매 demographic.
+4. **Gaming**: 매 acquired-similar.
+5. **B2B SaaS**: 매 industry.
+
+## 💻 패턴
+
+### Element-level template
+```typescript
+interface CreativeTemplate {
+  layout: 'hero' | 'carousel' | 'video';
+  slots: {
+    image: string;       // 매 URL
+    headline: string;
+    cta: string;
+    body: string;
+    logo: string;
+  };
+  brand_color: string;
+}
+
+function assemble(user: User, context: Context): CreativeTemplate {
+  return {
+    layout: pickLayout(user, context),
+    slots: {
+      image: pickImage(user.interests, context.season),
+      headline: pickHeadline(user.intent, context.event),
+      cta: pickCTA(user.funnel_stage),
+      body: pickBody(user.lang),
+      logo: BRAND_LOGO,
+    },
+    brand_color: BRAND_COLOR,
+  };
+}
+```
+
+### Contextual bandit (LinUCB)
+```python
+import numpy as np
+
+class LinUCB:
+    def __init__(self, n_arms, n_features, alpha=1.0):
+        self.alpha = alpha
+        self.A = [np.eye(n_features) for _ in range(n_arms)]
+        self.b = [np.zeros(n_features) for _ in range(n_arms)]
+    
+    def select(self, context):
+        scores = []
+        for a in range(len(self.A)):
+            theta = np.linalg.solve(self.A[a], self.b[a])
+            ucb = theta @ context + self.alpha * np.sqrt(
+                context @ np.linalg.solve(self.A[a], context)
+            )
+            scores.append(ucb)
+        return int(np.argmax(scores))
+    
+    def update(self, arm, context, reward):
+        self.A[arm] += np.outer(context, context)
+        self.b[arm] += reward * context
+
+# 매 each "arm" = 매 creative variant
+```
+
+### Generative ad (LLM headline)
+```python
+def generate_headlines(product, user_segment, n=10):
+    prompt = f"""Generate {n} ad headlines for "{product}" targeting {user_segment}.
+Tone: persuasive, concise (≤7 words). One per line."""
+    return llm.generate(prompt).split('\n')
+
+# 매 brand safety filter
+def is_safe(headline):
+    return classifier.predict(headline)['toxic'] < 0.1
+```
+
+### Diffusion image (variant)
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained('stabilityai/sdxl-turbo').to('cuda')
+
+def generate_hero(product, lifestyle, brand_style):
+    prompt = f"{product} in {lifestyle}, {brand_style}, professional photography"
+    return pipe(prompt, num_inference_steps=4).images[0]
+```
+
+### CTR predictor (Wide & Deep)
+```python
+class WideDeepCTR(nn.Module):
+    def __init__(self, n_cat, embed_dim=8, hidden=64):
+        super().__init__()
+        self.embeds = nn.ModuleList([nn.Embedding(c, embed_dim) for c in n_cat])
+        self.deep = nn.Sequential(
+            nn.Linear(len(n_cat) * embed_dim, hidden),
+            nn.ReLU(),
+            nn.Linear(hidden, 1),
+        )
+        self.wide = nn.Linear(sum(n_cat), 1)
+    
+    def forward(self, sparse_feats, deep_idx):
+        deep = torch.cat([e(deep_idx[:, i]) for i, e in enumerate(self.embeds)], dim=1)
+        return torch.sigmoid(self.wide(sparse_feats) + self.deep(deep))
+```
+
+### Multi-armed Thompson Sampling
+```python
+class ThompsonDCO:
+    def __init__(self, n_arms):
+        self.alpha = np.ones(n_arms)
+        self.beta = np.ones(n_arms)
+    
+    def select(self):
+        samples = np.random.beta(self.alpha, self.beta)
+        return int(np.argmax(samples))
+    
+    def update(self, arm, click):
+        if click: self.alpha[arm] += 1
+        else: self.beta[arm] += 1
+```
+
+### Real-time decisioning (DSP integration)
+```typescript
+// OpenRTB bid request
+async function decideAd(bidReq: BidRequest): Promise<AdResponse> {
+  const userVec = await fetchUserEmbed(bidReq.user.id);
+  const ctxVec = encodeContext(bidReq.imp[0]);
+  const variantId = bandit.select(concat(userVec, ctxVec));
+  const creative = assemble(variantId);
+  return {
+    bid: predictedCTR * cpc * 1000,  // 매 CPM
+    creative,
+  };
+}
+```
+
+### Brand safety guard
+```python
+def brand_safe(creative, context):
+    """매 placement adjacency check."""
+    if context.publisher_category in BLOCKED_CATEGORIES:
+        return False
+    if classifier.predict(creative.headline)['toxic'] > 0.05:
+        return False
+    if has_competitor_logo(creative.image):
+        return False
+    return True
+```
+
+### Frequency cap
+```python
+class FreqCap:
+    def __init__(self, redis, limit_per_day=5):
+        self.redis = redis
+        self.limit = limit_per_day
+    
+    def can_show(self, user_id, campaign_id):
+        key = f'fc:{user_id}:{campaign_id}:{today()}'
+        count = int(self.redis.get(key) or 0)
+        return count < self.limit
+    
+    def record(self, user_id, campaign_id):
+        key = f'fc:{user_id}:{campaign_id}:{today()}'
+        self.redis.incr(key)
+        self.redis.expire(key, 86400)
+```
+
+### Eval metric
+```python
+def evaluate(impressions):
+    return {
+        'CTR': sum(i.click for i in impressions) / len(impressions),
+        'CVR': sum(i.convert for i in impressions if i.click) / max(1, sum(i.click for i in impressions)),
+        'CPA': sum(i.spend for i in impressions) / max(1, sum(i.convert for i in impressions)),
+        'ROAS': sum(i.revenue for i in impressions) / sum(i.spend for i in impressions),
+    }
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Few variants | A/B test |
+| Many segments | Rules + bandit |
+| Many variants | Contextual bandit |
+| Long-term reward | RL |
+| Need fresh creative | Generative + brand-safe |
+| High-stakes | CTR predictor + rules |
+
+**기본값**: 매 element-level template + 매 contextual bandit + 매 generative refresh + 매 brand safety + 매 freq cap.
+
+## 🔗 Graph
+- 부모: [[Personalization]]
+- 응용: [[Multi-Armed-Bandit]]
+- Adjacent: [[Recommender-Systems]] · [[Diffusion-Models]]
+
+## 🤖 LLM 활용
+**언제**: 매 ad campaign. 매 personalization. 매 generative creative.
+**언제 X**: 매 brand-safety-strict (LLM 의 risk).
+
+## ❌ 안티패턴
+- **No frequency cap**: 매 user fatigue.
+- **Pure generative no filter**: 매 brand safety 의 violate.
+- **Static creative**: 매 fatigue 의 quickly.
+- **Bandit without features**: 매 personalization X.
+- **No measurement loop**: 매 optimization 의 stale.
+
+## 🧪 검증 / 중복
+- Verified (Google Ads DCO, Meta Advantage+, AdTech industry).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — DCO element + 매 LinUCB / Thompson / generative / CTR / brand-safety code |