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+import pymysql
+from typing import List, Tuple, Dict
+import random
+class AdRecall:
+    """
+    广告召回算法实现
+    专门用于召回广告类型的内容
+    """
+    
+    def __init__(self, db_config: dict):
+        """
+        初始化广告召回模型
+        
+        Args:
+            db_config: 数据库配置
+        """
+        self.db_config = db_config
+        self.ad_items = []
+        
+    def _get_ad_items(self):
+        """获取广告物品列表"""
+        conn = pymysql.connect(**self.db_config)
+        try:
+            cursor = conn.cursor()
+            
+            # 获取所有广告帖子，按热度和发布时间排序
+            cursor.execute("""
+                SELECT 
+                    p.id,
+                    p.heat,
+                    p.created_at,
+                    COUNT(DISTINCT b.user_id) as interaction_count,
+                    DATEDIFF(NOW(), p.created_at) as days_since_created
+                FROM posts p
+                LEFT JOIN behaviors b ON p.id = b.post_id
+                WHERE p.is_advertisement = 1 AND p.status = 'published'
+                GROUP BY p.id, p.heat, p.created_at
+                ORDER BY p.heat DESC, p.created_at DESC
+            """)
+            
+            results = cursor.fetchall()
+            
+            # 计算广告分数
+            items_with_scores = []
+            for row in results:
+                post_id, heat, created_at, interaction_count, days_since_created = row
+                
+                # 处理 None 值
+                heat = heat or 0
+                interaction_count = interaction_count or 0
+                days_since_created = days_since_created or 0
+                
+                # 广告分数计算：热度 + 交互数 - 时间惩罚
+                # 新发布的广告给予更高权重
+                freshness_bonus = max(0, 30 - days_since_created) / 30.0  # 30 天内的新鲜度奖励
+                
+                ad_score = (
+                    heat * 0.6 +
+                    interaction_count * 0.3 +
+                    freshness_bonus * 100  # 新鲜度奖励
+                )
+                
+                items_with_scores.append((post_id, ad_score))
+            
+            # 按广告分数排序
+            self.ad_items = sorted(items_with_scores, key=lambda x: x[1], reverse=True)
+            
+        finally:
+            cursor.close()
+            conn.close()
+    
+    def train(self):
+        """训练广告召回模型"""
+        print("开始获取广告物品...")
+        self._get_ad_items()
+        print(f"广告召回模型训练完成，共 {len (self.ad_items)} 个广告物品")
+    
+    def recall(self, user_id: int, num_items: int = 10) -> List[Tuple[int, float]]:
+        """
+        为用户召回广告物品
+        
+        Args:
+            user_id: 用户 ID
+            num_items: 召回物品数量
+            
+        Returns:
+            List of (item_id, score) tuples
+        """
+        # 如果尚未训练，先进行训练
+        if not hasattr(self, 'ad_items') or not self.ad_items:
+            self.train()
+        
+        # 获取用户已交互的广告，避免重复推荐
+        conn = pymysql.connect(**self.db_config)
+        try:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT DISTINCT b.post_id 
+                FROM behaviors b
+                JOIN posts p ON b.post_id = p.id
+                WHERE b.user_id = %s AND p.is_advertisement = 1
+                AND b.type IN ('like', 'favorite', 'comment', 'view')
+            """, (user_id,))
+            
+            user_interacted_ads = set(row[0] for row in cursor.fetchall())
+            
+            # 获取用户的兴趣标签（基于历史行为）
+            cursor.execute("""
+                SELECT t.name, COUNT(*) as count
+                FROM behaviors b
+                JOIN posts p ON b.post_id = p.id
+                JOIN post_tags pt ON p.id = pt.post_id
+                JOIN tags t ON pt.tag_id = t.id
+                WHERE b.user_id = %s AND b.type IN ('like', 'favorite', 'comment')
+                GROUP BY t.name
+                ORDER BY count DESC
+                LIMIT 10
+            """, (user_id,))
+            
+            user_interest_tags = set(row[0] for row in cursor.fetchall())
+            
+        finally:
+            cursor.close()
+            conn.close()
+        
+        # 过滤掉用户已交互的广告
+        filtered_ads = [
+            (item_id, score) for item_id, score in self.ad_items
+            if item_id not in user_interacted_ads
+        ]
+        
+        # 如果没有未交互的广告，但有广告数据，返回评分最高的广告（可能用户会再次感兴趣）
+        if not filtered_ads and self.ad_items:
+            print(f"用户 {user_id} 已与所有广告交互，返回评分最高的广告")
+            filtered_ads = self.ad_items[:num_items]
+        
+        # 如果用户有兴趣标签，可以进一步个性化广告推荐
+        if user_interest_tags and filtered_ads:
+            filtered_ads = self._personalize_ads(filtered_ads, user_interest_tags)
+        
+        return filtered_ads[:num_items]
+    
+    def _personalize_ads(self, ad_list: List[Tuple[int, float]], user_interest_tags: set) -> List[Tuple[int, float]]:
+        """
+        根据用户兴趣标签个性化广告推荐
+        
+        Args:
+            ad_list: 广告列表
+            user_interest_tags: 用户兴趣标签
+            
+        Returns:
+            个性化后的广告列表
+        """
+        conn = pymysql.connect(**self.db_config)
+        try:
+            cursor = conn.cursor()
+            
+            personalized_ads = []
+            for ad_id, ad_score in ad_list:
+                # 获取广告的标签
+                cursor.execute("""
+                    SELECT t.name
+                    FROM post_tags pt
+                    JOIN tags t ON pt.tag_id = t.id
+                    WHERE pt.post_id = %s
+                """, (ad_id,))
+                
+                ad_tags = set(row[0] for row in cursor.fetchall())
+                
+                # 计算标签匹配度
+                tag_match_score = len(ad_tags & user_interest_tags) / max(len(user_interest_tags), 1)
+                
+                # 调整广告分数
+                final_score = ad_score * (1 + tag_match_score)
+                personalized_ads.append((ad_id, final_score))
+            
+            # 重新排序
+            personalized_ads.sort(key=lambda x: x[1], reverse=True)
+            return personalized_ads
+            
+        finally:
+            cursor.close()
+            conn.close()
+    
+    def get_random_ads(self, num_items: int = 5) -> List[Tuple[int, float]]:
+        """
+        获取随机广告（用于多样性）
+        
+        Args:
+            num_items: 返回物品数量
+            
+        Returns:
+            List of (item_id, score) tuples
+        """
+        if len(self.ad_items) <= num_items:
+            return self.ad_items
+        
+        # 随机选择但倾向于高分广告
+        weights = [score for _, score in self.ad_items]
+        selected_indices = random.choices(
+            range(len(self.ad_items)), 
+            weights=weights, 
+            k=num_items
+        )
+        
+        return [self.ad_items[i] for i in selected_indices]