# Collaborative filtering recommendation engine
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class RecommendationEngine:
    def __init__(self, user_item_matrix):
        self.matrix = user_item_matrix
        self.similarity = cosine_similarity(user_item_matrix)
    
    def recommend(self, user_id, n_recommendations=5):
        user_similarities = self.similarity[user_id]
        similar_users = np.argsort(user_similarities)[::-1][1:11]
        
        # Get items from similar users
        recommendations = []
        for similar_user in similar_users:
            user_items = np.where(self.matrix[similar_user] > 0)[0]
            recommendations.extend(user_items)
        
        # Filter already purchased and return top N
        user_purchased = set(np.where(self.matrix[user_id] > 0)[0])
        recommendations = [r for r in recommendations if r not in user_purchased]
        
        return recommendations[:n_recommendations]

interface ChatbotCapabilities {
  orderTracking: boolean;
  productQuestions: boolean;
  returnProcessing: boolean;
  recommendationEngine: boolean;
  humanHandoff: boolean;
  multilingual: boolean;
}

const modernChatbot: ChatbotCapabilities = {
  orderTracking: true,
  productQuestions: true,
  returnProcessing: true,
  recommendationEngine: true,
  humanHandoff: true,
  multilingual: true,
};

const calculateOptimalPrice = ({
  basePrice,
  demand,
  inventory,
  competitorPrices,
  customerSegment,
}) => {
  let price = basePrice;
  
  // Demand adjustment
  if (demand > 0.8) price *= 1.15;
  else if (demand < 0.3) price *= 0.9;
  
  // Inventory pressure
  if (inventory < 10) price *= 1.1;
  else if (inventory > 100) price *= 0.95;
  
  // Competitive positioning
  const avgCompetitor = competitorPrices.reduce((a, b) => a + b) / competitorPrices.length;
  if (price > avgCompetitor * 1.2) price = avgCompetitor * 1.1;
  
  return Math.round(price * 100) / 100;
};

# Real-time fraud detection model
class FraudDetector:
    def __init__(self, model):
        self.model = model
        self.threshold = 0.7
    
    def assess_transaction(self, transaction):
        features = self.extract_features(transaction)
        risk_score = self.model.predict_proba(features)[0][1]
        
        return {
            "risk_score": risk_score,
            "action": self.determine_action(risk_score),
            "flags": self.identify_red_flags(transaction),
        }
    
    def determine_action(self, score):
        if score > 0.9:
            return "BLOCK"
        elif score > self.threshold:
            return "REVIEW"
        else:
            return "APPROVE"