Intelligent Recommendation Engine: Precisely Meet User Needs

class IntelligentRecommender:
    """Intelligent Recommendation Engine"""
    
    def __init__(self, llm_api, vector_db):
        self.llm = llm_api
        self.vector_db = vector_db
        self.user_profiles = {}
        
    def analyze_user_interests(self, user_id, behaviors):
        """Analyze user interests"""
        # Extract user behavior features
        viewed_items = behaviors.get('viewed', [])
        liked_items = behaviors.get('liked', [])
        purchased_items = behaviors.get('purchased', [])
        
        # Use LLM to analyze preferences
        prompt = f"""Analyze user preferences based on behavior data:
        
Viewed: {', '.join(viewed_items[:10])}
Liked: {', '.join(liked_items[:5])}
Purchased: {', '.join(purchased_items[:5])}

Summarize the user's:
1) Primary interest areas
2) Preferred styles
3) Price sensitivity
4) Potential needs"""
        
        analysis = self.llm.generate(prompt)
        
        # Generate user embedding
        user_vector = self.llm.create_embedding(analysis)
        
        self.user_profiles[user_id] = {
            'analysis': analysis,
            'vector': user_vector,
            'update_time': datetime.now()
        }
        
        return analysis
    
    def understand_content(self, item):
        """Understand content features"""
        prompt = f"""Analyze the following content features:
        
Title: {item['title']}
Description: {item['description']}
Category: {item['category']}

Extract:
1) Core topics
2) Style characteristics
3) Target audience
4) Key tags"""
        
        features = self.llm.generate(prompt)
        content_vector = self.llm.create_embedding(features)
        
        return {
            'features': features,
            'vector': content_vector
        }
    
    def recommend(self, user_id, num_items=10):
        """Generate recommendations"""
        # Get user profile
        if user_id not in self.user_profiles:
            return self.cold_start_recommend(user_id)
        
        user_profile = self.user_profiles[user_id]
        user_vector = user_profile['vector']
        
        # Vector similarity search
        candidates = self.vector_db.search(
            user_vector, 
            top_k=num_items * 3
        )
        
        # LLM reranking
        reranked = self.rerank_with_llm(
            user_profile['analysis'],
            candidates,
            num_items
        )
        
        return reranked
    
    def rerank_with_llm(self, user_analysis, candidates, top_k):
        """LLM reranking"""
        prompt = f"""User Profile:
{user_analysis}

Candidate List:
{self.format_candidates(candidates)}

Re-rank the candidates according to user preferences and select the top {top_k}.
Consider:
1) Interest match
2) Diversity
3) Novelty
4) Recency"""
        
        ranking = self.llm.generate(prompt)
        return self.parse_ranking(ranking, candidates)
    
    def explain_recommendation(self, user_id, item_id):
        """Explain recommendation"""
        user_profile = self.user_profiles[user_id]
        item_features = self.understand_content(self.get_item(item_id))
        
        prompt = f"""Explain why this item is recommended to the user:

User preferences: {user_profile['analysis']}
Recommended item: {item_features['features']}

Provide a simple, understandable explanation of the recommendation."""
        
        explanation = self.llm.generate(prompt)
        return explanation