Designing YouTube: A Video Platform at Planetary Scale

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┌─────────────────────────────────────────────────────────────────────────────────┐
│                     RECOMMENDATION SYSTEM ARCHITECTURE                           │
└─────────────────────────────────────────────────────────────────────────────────┘
 
     800M+ videos                                              10-50 videos
     in catalog                                                shown to user
         │                                                          ▲
         │                                                          │
         ▼                                                          │
┌─────────────────────────────────────────────────────────────────────────────────┐
│                           CANDIDATE GENERATION                                   │
│  "Find videos this user might be interested in"                                 │
│                                                                                  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐        │
│  │ Collaborative│  │   Content    │  │   Social    │  │  Trending    │        │
│  │  Filtering   │  │    Based     │  │   Graph     │  │  & Popular   │        │
│  │              │  │              │  │             │  │              │        │
│  │"Users like   │  │"Videos like  │  │"Friends are │  │"Viral now"   │        │
│  │ you watched" │  │ this one"    │  │ watching"   │  │              │        │
│  └──────────────┘  └──────────────┘  └──────────────┘  └──────────────┘        │
│                                                                                  │
│  Output: ~10,000 candidate videos per user                                      │
└─────────────────────────────────────┬───────────────────────────────────────────┘
                                      │
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│                              RANKING MODEL                                       │
│  "Score each candidate for this specific user"                                  │
│                                                                                  │
│  Features:                                                                       │
│  • User features: watch history, demographics, preferences                      │
│  • Video features: content, creator, freshness, popularity                      │
│  • Context features: time of day, device, location                              │
│                                                                                  │
│  Objective: Predict P(watch) × E(watch_time)                                    │
│                                                                                  │
│  Output: Ranked list of ~1000 videos with scores                                │
└─────────────────────────────────────┬───────────────────────────────────────────┘
                                      │
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│                              RE-RANKING / FILTERING                              │
│  "Apply business rules and diversity requirements"                              │
│                                                                                  │
│  • Remove watched videos, flagged content                                       │
│  • Apply age restrictions, geo-blocks                                           │
│  • Ensure diversity (don't show 10 videos from same creator)                    │
│  • Apply fairness constraints                                                   │
│  • Insert ads at appropriate positions                                          │
│                                                                                  │
│  Output: ~100 videos (more than needed for scroll depth)                        │
└─────────────────────────────────────┬───────────────────────────────────────────┘
                                      │
                                      ▼
┌─────────────────────────────────────────────────────────────────────────────────┐
│                              SERVING LAYER                                       │
│  "Deliver recommendations with low latency"                                     │
│                                                                                  │
│  • Assemble final response with thumbnails, metadata                            │
│  • A/B test assignment                                                          │
│  • Logging for training feedback                                                │
│  • p99 latency < 100ms                                                          │
│                                                                                  │
│  Output: 10-50 videos for initial page load                                     │
└─────────────────────────────────────────────────────────────────────────────────┘

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// ================================================================
// APPROACH 1: TWO-TOWER NEURAL RETRIEVAL
// ================================================================
// Learn embeddings for users and videos in the same vector space.
// Similar users and videos are close in embedding space.
 
interface TwoTowerModel {
  // User tower: encodes user history into embedding
  userEncoder: {
    inputs: [
      'watch_history',       // Last N watched videos
      'search_history',      // Recent searches
      'liked_videos',        // Explicit likes
      'channel_subscriptions',
      'demographics',        // Age, gender, country
    ];
    output: 'user_embedding[256]';
  };
  
  // Video tower: encodes video into embedding
  videoEncoder: {
    inputs: [
      'video_id',            // Learned embedding
      'title_embedding',     // NLP encoding of title
      'channel_id',
      'category',
      'tags',
      'visual_features',     // CNN features from thumbnails/frames
    ];
    output: 'video_embedding[256]';
  };
  
  // Similarity: dot product of embeddings
  // score(user, video) = user_embedding · video_embedding
}
 
// Training: positive examples are (user, watched_video) pairs
// Use in-batch negatives for efficiency
 
// Serving: user embedding computed at request time
// Video embeddings pre-computed and indexed for ANN search
 
class TwoTowerCandidateGenerator {
  private videoIndex: ApproximateNearestNeighborIndex;
  
  async generateCandidates(userId: string): Promise<VideoCandidate[]> {
    // 1. Compute user embedding (real-time)
    const userFeatures = await this.getUserFeatures(userId);
    const userEmbedding = await this.userEncoder.encode(userFeatures);
    
    // 2. Find nearest videos in embedding space (ANN search)
    // Using HNSW or ScaNN for efficient approximate search
    const candidates = await this.videoIndex.search(userEmbedding, {
      k: 1000,           // Return top 1000
      efSearch: 200,     // HNSW search parameter
    });
    
    return candidates.map(c => ({
      videoId: c.id,
      score: c.similarity,
      source: 'two-tower',
    }));
  }
}
 
// ================================================================
// APPROACH 2: COLLABORATIVE FILTERING
// ================================================================
// "Users who watched X also watched Y"
 
class MatrixFactorizationGenerator {
  // User-item interaction matrix factorized into:
  // R ≈ U × V^T
  // U: user latent factors [n_users × k]
  // V: video latent factors [n_videos × k]
  
  private userFactors: Map<string, Float32Array>;
  private videoIndex: ANNIndex<Float32Array>;
  
  async generateCandidates(userId: string): Promise<VideoCandidate[]> {
    const userVector = this.userFactors.get(userId);
    if (!userVector) {
      // Cold start: use popularity-based fallback
      return this.getPopularVideos();
    }
    
    // Find videos with high dot product
    return this.videoIndex.search(userVector, { k: 500 });
  }
}
 
// ================================================================
// APPROACH 3: CONTENT-BASED / CONTEXTUAL
// ================================================================
// "Videos similar to what you just watched"
 
class ContentBasedGenerator {
  async generateCandidates(
    userId: string,
    context: RequestContext
  ): Promise<VideoCandidate[]> {
    const candidates: VideoCandidate[] = [];
    
    // Seed videos: recently watched, currently watching
    const seedVideos = await this.getSeedVideos(userId, context);
    
    for (const seed of seedVideos) {
      // Find videos with similar content
      const similar = await this.findSimilarContent(seed, {
        methods: ['topic', 'creator', 'visual'],
      });
      
      candidates.push(...similar.map(v => ({
        videoId: v.id,
        score: v.similarity * seed.recency_weight,
        source: `content-similar-to-${seed.id}`,
      })));
    }
    
    return this.deduplicate(candidates);
  }
  
  private async findSimilarContent(
    video: Video,
    options: SimilarityOptions
  ): Promise<SimilarVideo[]> {
    const results: SimilarVideo[] = [];
    
    if (options.methods.includes('topic')) {
      // Same topic/category
      results.push(...await this.findByTopic(video.topics));
    }
    
    if (options.methods.includes('creator')) {
      // Same creator's other videos
      results.push(...await this.findByCreator(video.channelId));
    }
    
    if (options.methods.includes('visual')) {
      // Visually similar (thumbnail/content embeddings)
      results.push(...await this.findByVisualSimilarity(video.visualEmbedding));
    }
    
    return results;
  }
}
 
// ================================================================
// COMBINING MULTIPLE SOURCES
// ================================================================
 
class CandidateAggregator {
  private generators: CandidateGenerator[] = [
    new TwoTowerCandidateGenerator(),
    new MatrixFactorizationGenerator(),
    new ContentBasedGenerator(),
    new TrendingGenerator(),
    new SubscriptionGenerator(),
  ];
  
  async generateCandidates(request: RecommendationRequest): Promise<VideoCandidate[]> {
    // Run all generators in parallel
    const results = await Promise.all(
      this.generators.map(g => g.generateCandidates(request.userId, request.context))
    );
    
    // Merge and deduplicate
    const candidateMap = new Map<string, VideoCandidate>();
    
    for (const candidates of results) {
      for (const candidate of candidates) {
        const existing = candidateMap.get(candidate.videoId);
        if (existing) {
          // Boost score if nominated by multiple sources
          existing.score = Math.max(existing.score, candidate.score);
          existing.sources.push(candidate.source);
        } else {
          candidateMap.set(candidate.videoId, {
            ...candidate,
            sources: [candidate.source],
          });
        }
      }
    }
    
    // Sort by initial score and take top N
    return Array.from(candidateMap.values())
      .sort((a, b) => b.score - a.score)
      .slice(0, 10000);
  }
}

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// ================================================================
// RANKING MODEL ARCHITECTURE
// ================================================================
 
interface RankingModelInputs {
  // User features
  user: {
    userId: string;
    watchHistory: VideoId[];           // Last 100 videos
    searchHistory: string[];           // Last 50 searches
    demographics: {
      ageGroup: number;                // Age bucket
      gender: number;                  // Encoded gender
      country: number;                 // Country ID
    };
    preferences: {
      preferredCategories: number[];
      avgWatchDuration: number;        // User's typical watch length
      activityLevel: number;           // How active on platform
    };
  };
  
  // Video features
  video: {
    videoId: string;
    channelId: string;
    category: number;
    duration: number;
    uploadAge: number;                 // Days since upload
    popularity: {
      totalViews: number;
      recentViews: number;             // Last 7 days
      avgWatchPercentage: number;
      likeRatio: number;
    };
    content: {
      titleEmbedding: Float32Array;    // NLP encoding
      thumbnailEmbedding: Float32Array; // Vision encoding
      topics: number[];
    };
  };
  
  // Context features
  context: {
    timeOfDay: number;                 // Hour, encoded cyclically
    dayOfWeek: number;
    device: number;                    // Mobile, desktop, TV
    isWeekend: boolean;
    sessionDepth: number;              // How many videos already watched
  };
  
  // Interaction features (user × video)
  interaction: {
    userWatchedChannel: boolean;       // Has user watched this creator?
    userSubscribed: boolean;
    lastWatchedFromChannel: number;    // Days since
    topicOverlap: number;              // User preference × video topic match
  };
}
 
// Multi-objective output: predict multiple engagement signals
interface RankingModelOutputs {
  pClick: number;               // Probability of clicking
  pWatch: number;               // Probability of watching > 30 seconds
  expectedWatchTime: number;    // Expected watch duration
  pLike: number;                // Probability of liking
  pShare: number;               // Probability of sharing
  pSubscribe: number;           // Probability of subscribing to channel
}
 
// Final score combines multiple objectives
function computeFinalScore(outputs: RankingModelOutputs): number {
  // Weights determined by business objectives and A/B testing
  const weights = {
    watchTime: 0.6,      // Primary objective
    click: 0.1,          // Necessary but not sufficient
    like: 0.15,          // Explicit positive signal
    share: 0.1,          // High-value engagement
    subscribe: 0.05,     // Channel growth
  };
  
  // Normalize watch time prediction
  const normalizedWatchTime = outputs.expectedWatchTime / 300; // Assume 5 min max
  
  return (
    weights.watchTime * normalizedWatchTime * outputs.pWatch +
    weights.click * outputs.pClick +
    weights.like * outputs.pLike +
    weights.share * outputs.pShare +
    weights.subscribe * outputs.pSubscribe
  );
}
 
// ================================================================
// FEATURE ENGINEERING
// ================================================================
 
class FeatureExtractor {
  async extractFeatures(
    userId: string,
    videoId: string,
    context: RequestContext
  ): Promise<RankingModelInputs> {
    // Parallel feature fetching for low latency
    const [userFeatures, videoFeatures] = await Promise.all([
      this.getUserFeatures(userId),
      this.getVideoFeatures(videoId),
    ]);
    
    // Compute interaction features
    const interactionFeatures = this.computeInteractionFeatures(
      userFeatures, 
      videoFeatures
    );
    
    // Context features from request
    const contextFeatures = this.extractContextFeatures(context);
    
    return {
      user: userFeatures,
      video: videoFeatures,
      context: contextFeatures,
      interaction: interactionFeatures,
    };
  }
  
  private computeInteractionFeatures(
    user: UserFeatures,
    video: VideoFeatures
  ): InteractionFeatures {
    // Cross features that capture user-item interactions
    return {
      userWatchedChannel: user.watchedChannels.has(video.channelId),
      userSubscribed: user.subscriptions.has(video.channelId),
      lastWatchedFromChannel: this.daysSinceLastWatch(user, video.channelId),
      topicOverlap: this.computeTopicOverlap(user.preferredTopics, video.topics),
    };
  }
  
  private computeTopicOverlap(userTopics: Map<number, number>, videoTopics: number[]): number {
    // Weighted overlap based on user topic preferences
    let overlap = 0;
    for (const topic of videoTopics) {
      overlap += userTopics.get(topic) || 0;
    }
    return overlap;
  }
}
 
// ================================================================
// MODEL SERVING
// ================================================================
 
class RankingServer {
  private model: TensorFlowServingModel;
  private featureExtractor: FeatureExtractor;
  
  async rankCandidates(
    userId: string,
    candidates: VideoCandidate[],
    context: RequestContext
  ): Promise<RankedVideo[]> {
    // Batch feature extraction
    const features = await Promise.all(
      candidates.map(c => 
        this.featureExtractor.extractFeatures(userId, c.videoId, context)
      )
    );
    
    // Batch inference (much faster than individual requests)
    const predictions = await this.model.predict(features);
    
    // Compute final scores and rank
    const rankedVideos = candidates.map((candidate, i) => ({
      videoId: candidate.videoId,
      ...predictions[i],
      finalScore: computeFinalScore(predictions[i]),
      candidateSources: candidate.sources,
    }));
    
    return rankedVideos.sort((a, b) => b.finalScore - a.finalScore);
  }
}

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// ================================================================
// DATA COLLECTION AND LABELING
// ================================================================
 
interface InteractionEvent {
  userId: string;
  videoId: string;
  sessionId: string;
  timestamp: Date;
  
  // What the user did
  action: 'impression' | 'click' | 'watch' | 'like' | 'share' | 'subscribe';
  
  // For watch events
  watchDuration?: number;
  watchPercentage?: number;
  
  // Context at time of interaction
  device: string;
  country: string;
  source: 'home' | 'search' | 'related' | 'subscription' | 'notification';
  position: number;  // Position in recommendation list
}
 
// Positive examples: videos user engaged with
// Negative examples: videos shown but not clicked/watched
 
interface TrainingExample {
  features: RankingModelInputs;
  labels: {
    clicked: boolean;
    watchDuration: number;
    liked: boolean;
    shared: boolean;
    subscribed: boolean;
  };
  // Sample weight (for importance sampling)
  weight: number;
}
 
// ================================================================
// TRAINING DATA PROCESSING
// ================================================================
 
class TrainingDataPipeline {
  async generateTrainingData(date: Date): Promise<TrainingDataset> {
    // 1. Load interaction logs from data warehouse
    const interactions = await this.loadInteractions(date);
    
    // 2. Join with feature snapshots
    // (features as they were when interaction happened)
    const examples = await this.joinWithFeatures(interactions);
    
    // 3. Apply sampling strategies
    //    - Downsample negative examples (most impressions are not clicks)
    //    - Upsample rare positive signals (shares are rare but valuable)
    const balanced = this.applySampling(examples, {
      negativeRatio: 5,   // 5 negatives per positive
      rarePositiveBoost: 2, // 2x weight for shares/subscribes
    });
    
    // 4. Create training/validation split
    //    (temporal split: older for training, recent for validation)
    const { training, validation } = this.temporalSplit(balanced, {
      validationHours: 6,
    });
    
    return { training, validation };
  }
  
  private applySampling(
    examples: TrainingExample[],
    config: SamplingConfig
  ): TrainingExample[] {
    const positives = examples.filter(e => e.labels.clicked);
    const negatives = examples.filter(e => !e.labels.clicked);
    
    // Downsample negatives
    const sampledNegatives = this.sampleWithoutReplacement(
      negatives,
      positives.length * config.negativeRatio
    );
    
    // Adjust weights for sampled negatives
    const negativeWeight = negatives.length / sampledNegatives.length;
    for (const neg of sampledNegatives) {
      neg.weight *= negativeWeight;
    }
    
    // Boost rare positives
    for (const pos of positives) {
      if (pos.labels.shared || pos.labels.subscribed) {
        pos.weight *= config.rarePositiveBoost;
      }
    }
    
    return [...positives, ...sampledNegatives];
  }
}
 
// ================================================================
// MODEL TRAINING
// ================================================================
 
interface TrainingConfig {
  // Model architecture
  architecture: {
    userTowerLayers: number[];      // e.g., [512, 256, 128]
    videoTowerLayers: number[];
    crossLayers: number;            // Deep & Cross Network layers
    finalLayers: number[];          // e.g., [256, 128]
  };
  
  // Multi-task learning
  tasks: {
    click: { weight: 1.0, loss: 'binary_crossentropy' };
    watchTime: { weight: 0.5, loss: 'mse' };
    like: { weight: 0.3, loss: 'binary_crossentropy' };
    share: { weight: 0.2, loss: 'binary_crossentropy' };
  };
  
  // Training hyperparameters
  training: {
    batchSize: 4096;
    learningRate: 0.001;
    epochs: 5;                      // Few epochs, lots of data
    earlyStoppingPatience: 2;
  };
  
  // Regularization
  regularization: {
    dropout: 0.2;
    l2: 0.0001;
    embeddingL2: 0.00001;
  };
}
 
class ModelTrainer {
  async train(
    dataset: TrainingDataset,
    config: TrainingConfig
  ): Promise<TrainedModel> {
    // Build model
    const model = this.buildModel(config.architecture);
    
    // Multi-task loss function
    const lossWeights = Object.fromEntries(
      Object.entries(config.tasks).map(([task, cfg]) => [task, cfg.weight])
    );
    
    // Train with distributed training (data parallel)
    const strategy = new tf.distribute.MirroredStrategy();
    
    await strategy.scope(() => {
      model.compile({
        optimizer: tf.train.adam(config.training.learningRate),
        loss: this.buildMultiTaskLoss(config.tasks),
        lossWeights,
      });
      
      return model.fit(dataset.training, {
        epochs: config.training.epochs,
        batchSize: config.training.batchSize,
        validationData: dataset.validation,
        callbacks: [
          tf.callbacks.earlyStopping({ patience: config.training.earlyStoppingPatience }),
          new MetricsLogger(),
          new ModelCheckpointer(),
        ],
      });
    });
    
    return model;
  }
}
 
// ================================================================
// CONTINUOUS TRAINING
// ================================================================
 
class ContinuousTrainingPipeline {
  // Train new model daily
  async dailyTrainingJob(): Promise<void> {
    // 1. Generate training data from yesterday
    const dataset = await this.dataGenerator.generateTrainingData(yesterday());
    
    // 2. Train new model
    const newModel = await this.trainer.train(dataset, this.config);
    
    // 3. Evaluate on holdout set
    const metrics = await this.evaluator.evaluate(newModel, holdoutSet);
    
    // 4. Compare with current production model
    const comparison = await this.compare(newModel, this.productionModel);
    
    // 5. If better, gradually roll out
    if (comparison.improvement > 0.005) { // 0.5% improvement threshold
      await this.deployModel(newModel, {
        rolloutStrategy: 'gradual',
        initialPercent: 5,
        maxPercent: 100,
        rolloutDays: 7,
      });
    }
  }
}

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// ================================================================
// FEATURE STORE
// ================================================================
// Pre-computed features for low-latency serving
 
interface FeatureStore {
  // User features: updated hourly, cached in memory
  userFeatures: {
    storage: Redis | Memcached;
    ttl: '1 hour';
    precomputed: [
      'watch_history_embedding',
      'topic_preferences',
      'activity_level',
      'demographic_segment',
    ];
  };
  
  // Video features: updated on upload, cached long-term
  videoFeatures: {
    storage: Redis | Bigtable;
    ttl: 'indefinite';
    precomputed: [
      'content_embedding',
      'popularity_metrics',
      'creator_features',
    ];
  };
  
  // Real-time features: computed at request time
  realtimeFeatures: [
    'session_context',
    'recency_since_last_watch',
  ];
}
 
class FeatureServer {
  private userCache: Redis;
  private videoCache: Bigtable;
  
  async getUserFeatures(userId: string): Promise<UserFeatures> {
    // Try cache first
    const cached = await this.userCache.get(`user:${userId}`);
    if (cached) return JSON.parse(cached);
    
    // Fallback to cold-start features
    return this.getColdStartUserFeatures(userId);
  }
  
  async getVideoFeaturesBatch(videoIds: string[]): Promise<Map<string, VideoFeatures>> {
    // Batch request for efficiency
    const keys = videoIds.map(id => `video:${id}`);
    const results = await this.videoCache.getBatch(keys);
    
    const features = new Map();
    for (const [key, value] of results) {
      const videoId = key.replace('video:', '');
      features.set(videoId, JSON.parse(value));
    }
    
    return features;
  }
}
 
// ================================================================
// MODEL SERVING
// ================================================================
 
interface ServingCluster {
  // TensorFlow Serving or TensorRT for GPU inference
  modelServers: {
    replicas: 100;
    instanceType: 'GPU' | 'CPU';
    modelsLoaded: ['ranking_v23', 'ranking_v24'];  // A/B testing
    batchSize: 128;     // Batch requests for GPU efficiency
    maxConcurrency: 32;
  };
  
  // Load balancing
  loadBalancer: {
    strategy: 'least_connections';
    healthCheck: '/health';
    timeoutMs: 50;
  };
}
 
class ModelServer {
  private model: TFServingClient;
  
  async predict(features: RankingModelInputs[]): Promise<RankingModelOutputs[]> {
    // Batch for GPU efficiency
    const batches = this.createBatches(features, 128);
    
    const results: RankingModelOutputs[] = [];
    
    for (const batch of batches) {
      const batchResults = await this.model.predict({
        inputs: this.tensorize(batch),
        outputNames: ['p_click', 'p_watch', 'expected_watch_time', 'p_like'],
      });
      
      results.push(...this.parsePredictions(batchResults));
    }
    
    return results;
  }
}
 
// ================================================================
// REQUEST HANDLING
// ================================================================
 
class RecommendationService {
  private candidateGenerator: CandidateAggregator;
  private featureStore: FeatureServer;
  private ranker: ModelServer;
  private reranker: ReRanker;
  
  async getRecommendations(request: RecommendationRequest): Promise<RecommendationResponse> {
    const startTime = Date.now();
    
    try {
      // Stage 1: Candidate generation (target: 20ms)
      const t1 = Date.now();
      const candidates = await this.candidateGenerator.generateCandidates(request);
      this.logLatency('candidate_generation', Date.now() - t1);
      
      // Stage 2: Feature extraction (target: 15ms)
      const t2 = Date.now();
      const features = await this.extractFeatures(request.userId, candidates);
      this.logLatency('feature_extraction', Date.now() - t2);
      
      // Stage 3: Ranking (target: 25ms)
      const t3 = Date.now();
      const ranked = await this.ranker.rank(features);
      this.logLatency('ranking', Date.now() - t3);
      
      // Stage 4: Re-ranking and filtering (target: 10ms)
      const t4 = Date.now();
      const final = await this.reranker.rerank(ranked, request);
      this.logLatency('reranking', Date.now() - t4);
      
      // Stage 5: Response assembly (target: 5ms)
      const response = await this.assembleResponse(final, request);
      
      // Log total latency
      const totalLatency = Date.now() - startTime;
      this.logLatency('total', totalLatency);
      
      if (totalLatency > 100) {
        this.logSlowRequest(request, totalLatency);
      }
      
      return response;
      
    } catch (error) {
      // Fallback to cached/popular recommendations
      return this.getFallbackRecommendations(request);
    }
  }
  
  // Latency budget allocation
  private readonly LATENCY_BUDGETS = {
    candidateGeneration: 20,
    featureExtraction: 15,
    ranking: 25,
    reranking: 10,
    assembly: 5,
    network: 20,
    total: 100,
  };
}
 
// ================================================================
// CACHING LAYERS
// ================================================================
 
class RecommendationCache {
  // Cache recommendations for recently active users
  // Reduces cold-start latency and handles traffic spikes
  
  async getOrCompute(request: RecommendationRequest): Promise<RecommendationResponse> {
    const cacheKey = this.computeCacheKey(request);
    
    // Check cache (valid for ~5 minutes for active users)
    const cached = await this.cache.get(cacheKey);
    if (cached && this.isValid(cached, request)) {
      return cached;
    }
    
    // Compute fresh recommendations
    const fresh = await this.service.getRecommendations(request);
    
    // Cache for next request
    await this.cache.set(cacheKey, fresh, { ttl: 300 });
    
    return fresh;
  }
  
  private computeCacheKey(request: RecommendationRequest): string {
    // Cache key based on user + context
    // Fine-grained enough to be relevant, coarse enough to get hits
    return `rec:${request.userId}:${request.surface}:${Math.floor(Date.now() / 60000)}`;
  }
}

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// ================================================================
// COLD START: NEW USERS
// ================================================================
 
class ColdStartHandler {
  async getRecommendationsForNewUser(
    request: RecommendationRequest
  ): Promise<RecommendationResponse> {
    // 1. Use available signals
    const signals: ColdStartSignals = {
      device: request.device,        // Mobile users prefer shorter content
      country: request.country,      // Regional preferences
      language: request.language,    // Language-appropriate content
      referrer: request.referrer,    // What brought them here
      firstSearch: request.firstSearch, // If they searched first
    };
    
    // 2. Find similar users (demographic clustering)
    const similarUserCluster = await this.findSimilarCluster(signals);
    
    // 3. Get popular content for that cluster
    const popularInCluster = await this.getPopularForCluster(similarUserCluster);
    
    // 4. Mix with global trending for discovery
    const trending = await this.getTrendingForRegion(request.country);
    
    // 5. Include exploration content to learn preferences
    const exploration = await this.getExplorationContent(signals);
    
    return this.blend([
      { weight: 0.4, content: popularInCluster },
      { weight: 0.3, content: trending },
      { weight: 0.3, content: exploration },
    ]);
  }
}
 
// ================================================================
// COLD START: NEW VIDEOS
// ================================================================
 
class NewVideoHandler {
  async scoreNewVideo(
    video: Video,
    user: User
  ): Promise<number> {
    // No engagement history - use content features
    const contentScore = this.scoreByContent(video, user);
    
    // Creator track record
    const creatorScore = await this.getCreatorScore(video.channelId);
    
    // Similar videos' performance
    const similarScore = await this.getSimilarVideosScore(video);
    
    // Combine with uncertainty bonus (explore new videos)
    const explorationBonus = this.computeExplorationBonus(video.uploadAge);
    
    return (
      contentScore * 0.3 +
      creatorScore * 0.3 +
      similarScore * 0.3 +
      explorationBonus * 0.1
    );
  }
  
  private computeExplorationBonus(uploadAgeHours: number): number {
    // Higher bonus for very new videos (encourage exploration)
    // Decays over first 48 hours
    if (uploadAgeHours < 1) return 1.0;
    if (uploadAgeHours < 6) return 0.8;
    if (uploadAgeHours < 24) return 0.5;
    if (uploadAgeHours < 48) return 0.2;
    return 0;
  }
}
 
// ================================================================
// REAL-TIME TRENDING BOOST
// ================================================================
 
class TrendingBooster {
  private trendingScores: Map<string, TrendingScore> = new Map();
  
  // Update every few minutes
  async updateTrendingScores(): Promise<void> {
    // Get real-time view velocity
    const velocities = await this.getViewVelocities();
    
    for (const [videoId, velocity] of velocities) {
      const historical = await this.getHistoricalVelocity(videoId);
      
      // Score = how much faster than expected
      const ratio = velocity / (historical || 1);
      
      if (ratio > 5) {
        // 5x+ normal velocity = viral
        this.trendingScores.set(videoId, {
          boost: Math.min(ratio / 10, 2.0), // Cap at 2x boost
          reason: 'viral',
          detectedAt: Date.now(),
        });
      }
    }
  }
  
  applyTrendingBoost(video: RankedVideo): number {
    const trending = this.trendingScores.get(video.videoId);
    if (trending && Date.now() - trending.detectedAt < 3600000) {
      return video.finalScore * (1 + trending.boost);
    }
    return video.finalScore;
  }
}
 
// ================================================================
// DIVERSITY AND EXPLORATION
// ================================================================
 
class DiversityReRanker {
  rerank(videos: RankedVideo[]): RankedVideo[] {
    const result: RankedVideo[] = [];
    const usedCreators = new Set<string>();
    const usedCategories = new Map<string, number>();
    
    for (const video of videos) {
      // Penalize if creator already in results
      if (usedCreators.has(video.channelId)) {
        video.finalScore *= 0.7; // 30% penalty
      }
      
      // Penalize if too many from same category
      const categoryCount = usedCategories.get(video.category) || 0;
      if (categoryCount >= 3) {
        video.finalScore *= 0.8;
      }
      
      result.push(video);
      usedCreators.add(video.channelId);
      usedCategories.set(video.category, categoryCount + 1);
    }
    
    // Re-sort after penalties
    return result.sort((a, b) => b.finalScore - a.finalScore);
  }
  
  // Insert exploration slots
  injectExploration(videos: RankedVideo[], positions: number[]): RankedVideo[] {
    const exploration = this.getExplorationVideos();
    
    for (const pos of positions) {
      if (pos < videos.length) {
        videos.splice(pos, 0, exploration.shift()!);
      }
    }
    
    return videos;
  }
}

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// ================================================================
// POLICY-BASED FILTERING
// ================================================================
 
class SafetyFilter {
  async filter(videos: RankedVideo[], user: User): Promise<RankedVideo[]> {
    const filtered: RankedVideo[] = [];
    
    for (const video of videos) {
      const policy = await this.getPolicyStatus(video.videoId);
      
      // Remove policy-violating content
      if (policy.violated) continue;
      
      // Demote borderline content (not removed, but lower ranked)
      if (policy.borderline) {
        video.finalScore *= 0.3; // Heavy demotion
      }
      
      // Apply age restrictions
      if (policy.ageRestricted && user.age < 18) continue;
      
      // Apply user's content preferences
      if (user.restrictedMode && policy.matureContent) continue;
      
      filtered.push(video);
    }
    
    return filtered;
  }
}
 
// ================================================================
// RADICALIZATION PREVENTION
// ================================================================
 
class RadicalizationMonitor {
  async checkRecommendationPath(
    session: Session,
    newRecommendations: RankedVideo[]
  ): Promise<RankedVideo[]> {
    // Track content 'direction' in session
    const sessionPath = await this.getSessionContentPath(session);
    
    // Detect if trending toward extreme content
    const extremityTrend = this.computeExtremityTrend(sessionPath);
    
    if (extremityTrend > this.THRESHOLD) {
      // Intervene: inject moderate/authoritative content
      const intervention = await this.getInterventionContent(
        sessionPath,
        { type: 'authoritative', topic: sessionPath.mainTopic }
      );
      
      // Replace some recommendations with intervention
      return this.blend([
        { weight: 0.6, content: newRecommendations },
        { weight: 0.4, content: intervention },
      ]);
    }
    
    return newRecommendations;
  }
}
 
// ================================================================
// CREATOR FAIRNESS
// ================================================================
 
class CreatorFairnessHandler {
  async adjustForFairness(videos: RankedVideo[]): Promise<RankedVideo[]> {
    // Ensure small/new creators get some exposure
    const smallCreatorSlots = Math.ceil(videos.length * 0.1); // 10% of slots
    
    const smallCreatorVideos = videos.filter(v => 
      v.creatorSubscribers < 10000 && v.qualityScore > 0.6
    );
    
    const largeCreatorVideos = videos.filter(v => 
      v.creatorSubscribers >= 10000
    );
    
    // Interleave to ensure small creators appear
    return this.interleave(
      largeCreatorVideos.slice(0, videos.length - smallCreatorSlots),
      smallCreatorVideos.slice(0, smallCreatorSlots)
    );
  }
}
 
// ================================================================
// WELLBEING FEATURES
// ================================================================
 
class WellbeingHandler {
  async adjustForWellbeing(
    videos: RankedVideo[],
    session: Session
  ): Promise<RecommendationResponse> {
    const response: RecommendationResponse = { videos };
    
    // Check session length
    if (session.duration > 2 * 3600) { // 2+ hours
      // Prompt to take a break
      response.wellbeingPrompt = {
        type: 'break_reminder',
        message: "You've been watching for a while. Consider taking a break.",
      };
    }
    
    // Check time of day
    const userLocalHour = this.getUserLocalHour(session);
    if (userLocalHour >= 0 && userLocalHour < 5) { // Late night
      // Suggest bedtime reminder if enabled
      if (session.user.bedtimeReminders) {
        response.wellbeingPrompt = {
          type: 'bedtime_reminder',
          message: "It's getting late. Set a reminder to stop watching?",
        };
      }
    }
    
    return response;
  }
}