Merge/back_rhj/app/models/recall/ad_recall.py

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]