修复令牌验证逻辑，修改管理员dashboard,增加退出登录功能
Change-Id: I6a832763126dffd28733269044a1b1956c5b1106
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diff --git a/Merge/back_rhj/app/models/recall/ad_recall.py b/Merge/back_rhj/app/models/recall/ad_recall.py
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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]