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Jeason
2026-03-18 15:09:37 +08:00
parent d69d0158d8
commit adffbbca1b
6 changed files with 176 additions and 912 deletions
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86
modules/recommendation_engine.py
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@@ -93,6 +93,9 @@ class RecommendationEngine(BaseModule):
context = input_data.get('context', {})
try:
# 检查数据充足性
data_check = self._check_data_sufficiency(user_data)
# 生成完整餐食搭配推荐
meal_combinations = self._generate_meal_combinations(user_data, meal_type, preferences, context)
@@ -101,6 +104,7 @@ class RecommendationEngine(BaseModule):
'recommendations': meal_combinations,
'reasoning': self._generate_meal_reasoning(meal_combinations, user_data, meal_type),
'confidence': self._calculate_recommendation_confidence(user_data),
'data_sufficiency': data_check,
'metadata': {
'meal_type': meal_type,
'combination_count': len(meal_combinations)
@@ -117,27 +121,34 @@ class RecommendationEngine(BaseModule):
combinations = []
try:
# 1. 基于用户历史数据生成搭配
historical_combinations = self._generate_historical_combinations(user_data, meal_type)
combinations.extend(historical_combinations)
# 检查数据充足性
data_check = self._check_data_sufficiency(user_data)
# 2. 基于用户偏好生成个性化搭配
personalized_combinations = self._generate_personalized_combinations(user_data, meal_type, preferences)
combinations.extend(personalized_combinations)
# 数据充足：使用个性化推荐
if data_check['sufficient']:
# 1. 基于用户历史数据生成搭配
historical_combinations = self._generate_historical_combinations(user_data, meal_type)
combinations.extend(historical_combinations)
# 2. 基于用户偏好生成个性化搭配
personalized_combinations = self._generate_personalized_combinations(user_data, meal_type, preferences)
combinations.extend(personalized_combinations)
# 3. 如果个性化推荐不足，补充模板
if len(combinations) < 3:
template_combinations = self._generate_template_combinations(user_data, meal_type)
combinations.extend(template_combinations)
# 3. 基于相似用户生成搭配
similar_user_combinations = self._generate_similar_user_combinations(user_data, meal_type)
combinations.extend(similar_user_combinations)
# 4. 如果没有足够数据，使用模板生成
if len(combinations) < 3:
# 数据不足：使用通用模板推荐
else:
self.logger.info(f"用户数据不足({data_check['days']}天)，使用通用推荐")
template_combinations = self._generate_template_combinations(user_data, meal_type)
combinations.extend(template_combinations)
# 5. 去重和排序
# 去重和排序
combinations = self._deduplicate_and_rank_combinations(combinations, user_data)
# 6. 确保至少有一些推荐
# 确保至少有推荐
if not combinations:
combinations = self._generate_fallback_combinations(meal_type)
@@ -145,7 +156,6 @@ class RecommendationEngine(BaseModule):
except Exception as e:
self.logger.error(f"生成餐食搭配失败: {e}")
# 返回基础推荐
return self._generate_fallback_combinations(meal_type)
def _generate_historical_combinations(self, user_data: UserData, meal_type: str) -> List[Dict[str, Any]]:
@@ -666,18 +676,44 @@ class RecommendationEngine(BaseModule):
else:
return f"推荐{top_rec['food']}，基于您的个人偏好"
def _check_data_sufficiency(self, user_data: UserData) -> Dict[str, Any]:
"""检查数据充足性（按日期去重计算天数）"""
if not user_data.meals:
return {
'sufficient': False,
'days': 0,
'meals': 0,
'message': '暂无餐食记录，将提供通用推荐'
}
# 按日期去重计算天数
unique_dates = set(meal.get('date') for meal in user_data.meals if meal.get('date'))
meal_count = len(user_data.meals)
day_count = len(unique_dates)
return {
'sufficient': day_count >= 3,
'days': day_count,
'meals': meal_count,
'message': f'已收集{day_count}天{meal_count}餐数据' + ('，数据充足' if day_count >= 3 else '，建议继续记录')
}
def _calculate_recommendation_confidence(self, user_data: UserData) -> float:
"""计算推荐置信度"""
meal_count = len(user_data.meals)
data_check = self._check_data_sufficiency(user_data)
meal_count = data_check['meals']
day_count = data_check['days']
feedback_count = len(user_data.feedback)
# 基于数据量计算置信度
if meal_count >= 15 and feedback_count >= 5:
if day_count >= 5 and feedback_count >= 5:
return 0.9
elif meal_count >= 10 and feedback_count >= 3:
elif day_count >= 3 and meal_count >= 7:
return 0.7
elif meal_count >= 5:
return 0.5
elif day_count >= 3:
return 0.6
elif meal_count >= 3:
return 0.4
else:
return 0.3
@@ -991,7 +1027,7 @@ if __name__ == "__main__":
# 初始化应用
config = BaseConfig()
if initialize_app(config):
print("✅ 应用初始化成功")
print(" 应用初始化成功")
# 测试餐食推荐
test_user_id = "test_user_001"
@@ -999,7 +1035,7 @@ if __name__ == "__main__":
result = generate_meal_recommendations(test_user_id, "lunch", {"taste": "sweet"})
if result and result.get('success'):
recommendations = result.get('recommendations', [])
print(f"✅ 餐食推荐成功，推荐了{len(recommendations)}种食物")
print(f" 餐食推荐成功，推荐了{len(recommendations)}种食物")
for rec in recommendations[:3]:
print(f" - {rec['food']}: {rec.get('score', 0):.2f}")
@@ -1007,10 +1043,10 @@ if __name__ == "__main__":
result = find_similar_foods(test_user_id, "米饭")
if result and result.get('success'):
similar_foods = result.get('similar_foods', [])
print(f"✅ 相似食物查找成功，找到{len(similar_foods)}种相似食物")
print(f" 相似食物查找成功，找到{len(similar_foods)}种相似食物")
# 清理应用
cleanup_app()
print("✅ 应用清理完成")
print(" 应用清理完成")
else:
print("❌ 应用初始化失败")
print(" 应用初始化失败")