AI智能短链接跳转系统：个性化路由、意图识别与实时优化

rules:
  - name: "app_install_priority"
    priority: 100
    condition: |
      device.os == "iOS" AND 
      app.not_installed AND 
      user.country == "CN"
    action:
      destination: "https://apps.apple.com/cn/app/id123456"
      utm_campaign: "ios_install_cn"
    
  - name: "retargeting_vip"
    priority: 90
    condition: |
      user.segment == "VIP" AND 
      user.days_since_last_purchase <= 7
    action:
      destination: "https://shop.com/vip-exclusive"
      
  - name: "weather_adaptive"
    priority: 50
    condition: |
      geo.city == "Beijing" AND
      weather.temperature < 10
    action:
      destination: "https://shop.com/winter-collection"

package engine

type Rule struct {
    Name       string
    Priority   int
    Condition  *expr.Expr
    Action     Action
}

type Context struct {
    Device   DeviceInfo
    User     UserProfile
    Geo      GeoInfo
    Time     time.Time
    Campaign CampaignInfo
}

type SmartRouter struct {
    rules []Rule
}

func (r *SmartRouter) Route(ctx Context) (Action, error) {
    // 按优先级排序
    sort.Slice(r.rules, func(i, j int) bool {
        return r.rules[i].Priority > r.rules[j].Priority
    })
    
    for _, rule := range r.rules {
        result, err := rule.Condition.Eval(ctx)
        if err != nil {
            continue
        }
        if result.(bool) {
            return rule.Action, nil
        }
    }
    
    // 兜底策略
    return r.defaultAction, nil
}

import lightgbm as lgb

class ConversionPredictor:
    def __init__(self):
        self.model = lgb.Booster(model_file='conversion_model.txt')
    
    def predict(self, user_features, candidate_pages):
        """对多个候选落地页预测转化率"""
        scores = []
        for page in candidate_pages:
            features = self._combine(user_features, page.features)
            prob = self.model.predict(features)[0]
            scores.append((page.url, prob))
        
        # 按转化率排序
        return sorted(scores, key=lambda x: x[1], reverse=True)

type FeatureExtractor struct{}

func (e *FeatureExtractor) Extract(ctx Context) map[string]interface{} {
    features := map[string]interface{}{
        // 时间特征
        "hour_of_day":   ctx.Time.Hour(),
        "day_of_week":   int(ctx.Time.Weekday()),
        "is_weekend":    ctx.Time.Weekday() >= 5,
        "is_holiday":    holidayChecker.IsHoliday(ctx.Time),
        
        // 设备特征
        "device_os":     ctx.Device.OS,
        "device_brand":  ctx.Device.Brand,
        "screen_size":   ctx.Device.ScreenSize,
        "is_tablet":     ctx.Device.ScreenSize > 768,
        
        // 用户特征
        "user_age_days": time.Since(ctx.User.CreatedAt).Hours() / 24,
        "purchase_count": ctx.User.PurchaseCount,
        "avg_order_value": ctx.User.AOV,
        "last_session_hours": time.Since(ctx.User.LastSession).Hours(),
        
        // 地理特征
        "country":       ctx.Geo.Country,
        "city_tier":     ctx.Geo.CityTier, // 一线城市=1
        "temperature":   ctx.Geo.Weather.Temperature,
        
        // 联合特征
        "os_x_hour":     fmt.Sprintf("%s_%d", ctx.Device.OS, ctx.Time.Hour()),
    }
    return features
}

experiments:
  - id: "exp_2025_landing_page"
    name: "落地页版本对比"
    status: "running"
    traffic_split:
      control: 0.4
      variants:
        - name: "v1_video_hero"
          weight: 0.3
          config:
            destination: "https://brand.com/landing-v1"
        - name: "v2_social_proof"
          weight: 0.3
          config:
            destination: "https://brand.com/landing-v2"
    metrics:
      primary: "conversion_rate"
      secondary: ["bounce_rate", "avg_session_duration"]
    winner_criteria:
      min_sample_size: 10000
      confidence_level: 0.95
      min_uplift: 0.05

type ExperimentTracker struct {
    redis *redis.Client
}

func (t *ExperimentTracker) RecordConversion(expID, variant string) {
    pipe := t.redis.Pipeline()
    pipe.Incr(ctx, fmt.Sprintf("exp:%s:%s:conversions", expID, variant))
    pipe.Incr(ctx, fmt.Sprintf("exp:%s:%s:total", expID, variant))
    pipe.Expire(ctx, fmt.Sprintf("exp:%s:*", expID), 7*24*time.Hour)
    pipe.Exec(ctx)
}

func (t *ExperimentTracker) GetWinner(expID string) (string, error) {
    // 计算各variant的转化率+置信区间
    // 返回达到显著性的winner，或"undetermined"
    variants := []string{"control", "v1", "v2"}
    return thompsonSampling(expID, variants), nil
}

func (p *ConversionPredictor) UpdateFeature(userID string, event ClickEvent) {
    // Kafka 消费实时事件，更新用户画像
    userProfile := p.cache.Get(userID)
    
    switch event.Type {
    case "click":
        userProfile.ClickHistory = append(userProfile.ClickHistory, event)
    case "purchase":
        userProfile.PurchaseCount++
        userProfile.LastPurchase = event.Timestamp
        userProfile.AOV = weightedAvg(userProfile.AOV, event.Amount)
    case "cart_add":
        userProfile.CartAffinity = updateAffinity(userProfile.CartAffinity, event.ItemCategory)
    }
    
    p.cache.Set(userID, userProfile, 24*time.Hour)
}

场景: 双11主会场短链

规则:
  - 时间: 0:00-2:00 → 秒杀专场（紧迫感最强时段）
  - 时间: 10:00-14:00 → 品类精选（摸鱼购物高峰）
  - 用户: 近7天加购未付款 → 购物车召回页（优惠提醒）
  - 用户: 高LTV用户 → 专属客服通道（1对1服务）
  - 地域: 二三线城市 → 低价爆款（价格敏感）
  - 设备: iPhone → Apple Pay快捷支付页