AI Personalization for Maximum Engagement: The Advanced Implementation Guide

# Personalization sophistication levels
personalization_levels = {
    'level_1_basic': {
        'description': 'Static demographic segmentation',
        'techniques': ['age_groups', 'location_based', 'device_type'],
        'engagement_lift': '10-20%'
    },
    'level_2_behavioral': {
        'description': 'Historical behavior analysis',
        'techniques': ['purchase_history', 'browsing_patterns', 'preference_tracking'],
        'engagement_lift': '30-50%'
    },
    'level_3_predictive': {
        'description': 'ML-powered prediction and recommendation',
        'techniques': ['collaborative_filtering', 'content_based_rec', 'hybrid_models'],
        'engagement_lift': '80-150%'
    },
    'level_4_adaptive': {
        'description': 'Real-time behavioral adaptation',
        'techniques': ['reinforcement_learning', 'contextual_bandits', 'dynamic_optimization'],
        'engagement_lift': '200-400%'
    },
    'level_5_anticipatory': {
        'description': 'Predictive intent and proactive personalization',
        'techniques': ['neural_networks', 'transformer_models', 'multi_modal_ai'],
        'engagement_lift': '400%+'
    }
}

import asyncio
import json
from dataclasses import dataclass
from typing import Dict, List, Optional

@dataclass
class UserEvent:
    user_id: str
    event_type: str
    timestamp: int
    properties: Dict
    context: Dict

class RealTimePersonalizationEngine:
    def __init__(self):
        self.user_profiles = {}
        self.ml_models = self.load_models()
        self.feature_store = FeatureStore()

    async def process_user_event(self, event: UserEvent):
        """
        Processes user event and updates personalization in real-time
        """
        # Extract behavioral signals
        behavioral_features = self.extract_behavioral_features(event)

        # Update user profile
        await self.update_user_profile(event.user_id, behavioral_features)

        # Generate personalized recommendations
        recommendations = await self.generate_recommendations(
            event.user_id,
            event.context
        )

        # Trigger real-time experience updates
        await self.update_user_experience(event.user_id, recommendations)

        return recommendations

    def extract_behavioral_features(self, event: UserEvent) -> Dict:
        """
        Extracts meaningful features from user behavior
        """
        features = {}

        # Engagement signals
        if event.event_type == 'page_view':
            features['page_category'] = event.properties.get('category')
            features['time_on_page'] = event.properties.get('duration', 0)
            features['scroll_depth'] = event.properties.get('scroll_percentage', 0)

        elif event.event_type == 'click':
            features['click_type'] = event.properties.get('element_type')
            features['click_position'] = event.properties.get('position')
            features['click_context'] = event.properties.get('context')

        elif event.event_type == 'search':
            features['search_query'] = event.properties.get('query')
            features['search_results_clicked'] = event.properties.get('results_clicked', 0)
            features['search_category'] = event.properties.get('category')

        # Contextual features
        features['device_type'] = event.context.get('device_type')
        features['session_depth'] = event.context.get('session_page_count', 1)
        features['time_of_day'] = self.extract_time_features(event.timestamp)

        return features

    async def generate_recommendations(self, user_id: str, context: Dict) -> Dict:
        """
        Generates personalized recommendations using ML models
        """
        user_profile = await self.get_user_profile(user_id)

        # Content recommendations
        content_recs = self.ml_models['content_recommender'].predict(
            user_features=user_profile,
            context_features=context,
            num_recommendations=10
        )

        # Product recommendations (if e-commerce)
        if 'product_recommender' in self.ml_models:
            product_recs = self.ml_models['product_recommender'].predict(
                user_id=user_id,
                context=context
            )
        else:
            product_recs = []

        # Next action predictions
        next_actions = self.ml_models['action_predictor'].predict_next_actions(
            user_profile=user_profile,
            current_context=context
        )

        return {
            'content_recommendations': content_recs,
            'product_recommendations': product_recs,
            'suggested_actions': next_actions,
            'personalization_confidence': self.calculate_confidence(user_profile),
            'recommendation_reason': self.generate_explanation(user_profile, content_recs)
        }

class MultiModalUserModel:
    def __init__(self):
        self.text_encoder = self.load_text_encoder()  # BERT/RoBERTa
        self.behavior_encoder = self.load_behavior_encoder()  # Neural network
        self.image_encoder = self.load_image_encoder()  # ResNet/Vision Transformer
        self.fusion_model = self.load_fusion_model()  # Multi-modal fusion

    def create_unified_user_representation(self, user_data: Dict) -> np.ndarray:
        """
        Creates unified user representation from multiple modalities
        """
        representations = {}

        # Text-based understanding (searches, reviews, comments)
        if 'text_interactions' in user_data:
            text_features = self.encode_text_behavior(user_data['text_interactions'])
            representations['text'] = text_features

        # Behavioral patterns (clicks, navigation, time spent)
        if 'behavioral_data' in user_data:
            behavior_features = self.encode_behavior_patterns(user_data['behavioral_data'])
            representations['behavior'] = behavior_features

        # Visual preferences (images engaged with, visual content)
        if 'visual_interactions' in user_data:
            visual_features = self.encode_visual_preferences(user_data['visual_interactions'])
            representations['visual'] = visual_features

        # Temporal patterns (usage times, seasonal behavior)
        if 'temporal_data' in user_data:
            temporal_features = self.encode_temporal_patterns(user_data['temporal_data'])
            representations['temporal'] = temporal_features

        # Fuse all modalities into unified representation
        unified_representation = self.fusion_model.fuse_modalities(representations)

        return unified_representation

    def encode_text_behavior(self, text_data: List[str]) -> np.ndarray:
        """
        Encodes user text interactions into dense representations
        """
        # Combine all user text
        combined_text = ' '.join(text_data)

        # Extract semantic features
        text_embedding = self.text_encoder.encode(combined_text)

        # Extract topic preferences
        topic_distribution = self.extract_topic_preferences(combined_text)

        # Extract sentiment patterns
        sentiment_patterns = self.analyze_sentiment_patterns(text_data)

        return np.concatenate([text_embedding, topic_distribution, sentiment_patterns])

    def encode_behavior_patterns(self, behavior_data: Dict) -> np.ndarray:
        """
        Encodes behavioral patterns into feature vectors
        """
        features = []

        # Navigation patterns
        navigation_features = self.analyze_navigation_patterns(
            behavior_data.get('page_sequences', [])
        )
        features.append(navigation_features)

        # Engagement patterns
        engagement_features = self.analyze_engagement_patterns(
            behavior_data.get('interaction_data', {})
        )
        features.append(engagement_features)

        # Purchase/conversion patterns
        if 'conversion_data' in behavior_data:
            conversion_features = self.analyze_conversion_patterns(
                behavior_data['conversion_data']
            )
            features.append(conversion_features)

        return np.concatenate(features)

class ContextualPersonalizationEngine:
    def __init__(self):
        self.context_models = {
            'temporal': TemporalContextModel(),
            'situational': SituationalContextModel(),
            'device': DeviceContextModel(),
            'location': LocationContextModel(),
            'social': SocialContextModel()
        }

    async def adapt_experience(self, user_id: str, raw_context: Dict) -> Dict:
        """
        Adapts user experience based on comprehensive context analysis
        """
        # Enrich context with AI insights
        enriched_context = await self.enrich_context(raw_context)

        # Analyze current user state
        user_state = await self.analyze_user_state(user_id, enriched_context)

        # Generate context-aware adaptations
        adaptations = {}

        # Content adaptation
        adaptations['content'] = await self.adapt_content(user_state, enriched_context)

        # UI/UX adaptation
        adaptations['interface'] = await self.adapt_interface(user_state, enriched_context)

        # Messaging adaptation
        adaptations['messaging'] = await self.adapt_messaging(user_state, enriched_context)

        # Timing optimization
        adaptations['timing'] = await self.optimize_timing(user_state, enriched_context)

        return adaptations

    async def enrich_context(self, raw_context: Dict) -> Dict:
        """
        Enriches basic context with AI-derived insights
        """
        enriched = raw_context.copy()

        # Temporal context enrichment
        temporal_insights = self.context_models['temporal'].analyze(
            timestamp=raw_context.get('timestamp'),
            timezone=raw_context.get('timezone')
        )
        enriched['temporal_insights'] = temporal_insights

        # Situational context analysis
        situational_insights = self.context_models['situational'].analyze(
            session_data=raw_context.get('session_data', {}),
            referrer=raw_context.get('referrer'),
            campaign_data=raw_context.get('campaign_data', {})
        )
        enriched['situational_insights'] = situational_insights

        # Device context analysis
        device_insights = self.context_models['device'].analyze(
            device_type=raw_context.get('device_type'),
            screen_size=raw_context.get('screen_size'),
            connection_speed=raw_context.get('connection_speed')
        )
        enriched['device_insights'] = device_insights

        return enriched

    async def adapt_content(self, user_state: Dict, context: Dict) -> Dict:
        """
        Adapts content based on user state and context
        """
        adaptations = {}

        # Content complexity adaptation
        if context['device_insights']['is_mobile']:
            adaptations['content_format'] = 'mobile_optimized'
            adaptations['content_length'] = 'shorter'
        else:
            adaptations['content_format'] = 'full_featured'
            adaptations['content_length'] = 'detailed'

        # Context-based content selection
        if context['temporal_insights']['is_work_hours']:
            adaptations['content_type'] = 'professional_focused'
        elif context['temporal_insights']['is_leisure_time']:
            adaptations['content_type'] = 'entertainment_focused'

        # Urgency-based adaptation
        if user_state.get('intent_urgency', 'low') == 'high':
            adaptations['content_priority'] = 'action_oriented'
            adaptations['cta_prominence'] = 'high'

        return adaptations

import tensorflow as tf
from tensorflow.keras import layers, Model

class NeuralCollaborativeFiltering(Model):
    def __init__(self, num_users, num_items, embedding_size=50, hidden_layers=[128, 64]):
        super().__init__()

        self.num_users = num_users
        self.num_items = num_items
        self.embedding_size = embedding_size

        # Embedding layers
        self.user_embedding = layers.Embedding(num_users, embedding_size)
        self.item_embedding = layers.Embedding(num_items, embedding_size)
        self.user_bias = layers.Embedding(num_users, 1)
        self.item_bias = layers.Embedding(num_items, 1)

        # Neural MF layers
        self.hidden_layers = []
        for units in hidden_layers:
            self.hidden_layers.append(layers.Dense(units, activation='relu'))
            self.hidden_layers.append(layers.Dropout(0.2))

        self.output_layer = layers.Dense(1, activation='sigmoid')

        # Global bias
        self.global_bias = tf.Variable(0.0, trainable=True)

    def call(self, inputs, training=None):
        user_ids, item_ids = inputs

        # Get embeddings
        user_vec = self.user_embedding(user_ids)
        item_vec = self.item_embedding(item_ids)

        # Get biases
        user_bias = self.user_bias(user_ids)
        item_bias = self.item_bias(item_ids)

        # Concatenate embeddings for neural MF
        concat_vec = layers.concatenate([user_vec, item_vec])

        # Pass through neural network
        x = concat_vec
        for layer in self.hidden_layers:
            x = layer(x, training=training)

        # Final prediction
        rating = self.output_layer(x)

        # Add biases
        rating = rating + user_bias + item_bias + self.global_bias

        return tf.squeeze(rating)

    def recommend_items(self, user_id, num_recommendations=10, exclude_seen=True):
        """
        Generates item recommendations for a user
        """
        # Get all items
        all_items = tf.range(self.num_items)
        user_ids = tf.fill([self.num_items], user_id)

        # Predict ratings for all items
        predictions = self.call([user_ids, all_items])

        # Get top N recommendations
        top_items = tf.nn.top_k(predictions, k=num_recommendations)

        return {
            'item_ids': top_items.indices.numpy(),
            'predicted_ratings': top_items.values.numpy(),
            'confidence_scores': tf.nn.softmax(top_items.values).numpy()
        }

import numpy as np
from typing import Dict, List, Optional

class ContextualBandit:
    """
    Contextual multi-armed bandit for dynamic content personalization
    """

    def __init__(self, num_arms: int, context_dim: int, alpha: float = 1.0):
        self.num_arms = num_arms
        self.context_dim = context_dim
        self.alpha = alpha

        # Initialize model parameters for each arm
        self.A = [np.eye(context_dim) for _ in range(num_arms)]  # Covariance matrices
        self.b = [np.zeros(context_dim) for _ in range(num_arms)]  # Reward vectors
        self.theta = [np.zeros(context_dim) for _ in range(num_arms)]  # Parameter vectors

    def select_arm(self, context: np.ndarray) -> int:
        """
        Selects optimal arm (content variant) given context
        """
        p_values = []

        for arm in range(self.num_arms):
            # Update parameter estimate
            self.theta[arm] = np.linalg.solve(self.A[arm], self.b[arm])

            # Calculate upper confidence bound
            A_inv = np.linalg.inv(self.A[arm])
            confidence_interval = self.alpha * np.sqrt(
                context.T @ A_inv @ context
            )

            # Expected reward + confidence interval
            expected_reward = context.T @ self.theta[arm]
            p_value = expected_reward + confidence_interval

            p_values.append(p_value)

        # Select arm with highest upper confidence bound
        return int(np.argmax(p_values))

    def update(self, chosen_arm: int, context: np.ndarray, reward: float):
        """
        Updates model based on observed reward
        """
        # Update covariance matrix
        self.A[chosen_arm] += np.outer(context, context)

        # Update reward vector
        self.b[chosen_arm] += reward * context

class PersonalizationBandit:
    """
    Personalization system using contextual bandits
    """

    def __init__(self, content_variants: List[Dict]):
        self.content_variants = content_variants
        self.num_variants = len(content_variants)

        # Initialize bandit for each user segment
        self.bandits = {}
        self.context_dim = 20  # Dimensionality of user context

    def get_personalized_content(self, user_id: str, context_features: Dict) -> Dict:
        """
        Selects personalized content using bandit optimization
        """
        # Get or create bandit for user segment
        user_segment = self.get_user_segment(user_id)
        if user_segment not in self.bandits:
            self.bandits[user_segment] = ContextualBandit(
                num_arms=self.num_variants,
                context_dim=self.context_dim
            )

        # Convert context to feature vector
        context_vector = self.vectorize_context(context_features)

        # Select optimal content variant
        chosen_variant = self.bandits[user_segment].select_arm(context_vector)

        return {
            'content': self.content_variants[chosen_variant],
            'variant_id': chosen_variant,
            'user_segment': user_segment,
            'context_vector': context_vector.tolist()
        }

    def record_engagement(self, user_id: str, variant_id: int,
                         context_vector: np.ndarray, engagement_score: float):
        """
        Records engagement outcome to improve future recommendations
        """
        user_segment = self.get_user_segment(user_id)

        if user_segment in self.bandits:
            self.bandits[user_segment].update(
                chosen_arm=variant_id,
                context=context_vector,
                reward=engagement_score
            )

    def vectorize_context(self, context_features: Dict) -> np.ndarray:
        """
        Converts context dictionary to numerical vector
        """
        # Feature engineering for context
        vector = np.zeros(self.context_dim)

        # Time-based features
        vector[0] = context_features.get('hour_of_day', 0) / 24.0
        vector[1] = context_features.get('day_of_week', 0) / 7.0

        # Device features
        vector[2] = 1.0 if context_features.get('device_type') == 'mobile' else 0.0
        vector[3] = 1.0 if context_features.get('device_type') == 'tablet' else 0.0

        # Behavioral features
        vector[4] = min(context_features.get('session_page_count', 0) / 10.0, 1.0)
        vector[5] = min(context_features.get('time_on_site', 0) / 3600.0, 1.0)

        # Engagement history features
        vector[6] = context_features.get('avg_engagement_score', 0.5)
        vector[7] = context_features.get('conversion_likelihood', 0.0)

        # Content preference features
        preferences = context_features.get('content_preferences', {})
        vector[8:18] = [preferences.get(f'category_{i}', 0.0) for i in range(10)]

        # Seasonal/trending features
        vector[18] = context_features.get('trending_score', 0.0)
        vector[19] = context_features.get('seasonal_relevance', 0.0)

        return vector

import torch
import torch.nn as nn
from transformers import GPT2Model, GPT2Config

class UserBehaviorTransformer(nn.Module):
    """
    Transformer model for understanding sequential user behavior
    """

    def __init__(self, vocab_size, max_sequence_length=512, d_model=768):
        super().__init__()

        self.max_seq_length = max_sequence_length
        self.d_model = d_model

        # GPT-2 based transformer
        config = GPT2Config(
            vocab_size=vocab_size,
            n_positions=max_sequence_length,
            n_embd=d_model,
            n_layer=8,
            n_head=12
        )

        self.transformer = GPT2Model(config)

        # Prediction heads
        self.next_action_head = nn.Linear(d_model, vocab_size)
        self.engagement_head = nn.Linear(d_model, 1)
        self.intent_classification_head = nn.Linear(d_model, 10)  # 10 intent classes

    def forward(self, input_ids, attention_mask=None):
        # Get transformer outputs
        transformer_outputs = self.transformer(
            input_ids=input_ids,
            attention_mask=attention_mask
        )

        sequence_output = transformer_outputs.last_hidden_state

        # Predictions
        next_action_logits = self.next_action_head(sequence_output)
        engagement_scores = torch.sigmoid(self.engagement_head(sequence_output))
        intent_logits = self.intent_classification_head(sequence_output[:, -1, :])

        return {
            'next_action_logits': next_action_logits,
            'engagement_scores': engagement_scores,
            'intent_logits': intent_logits
        }

    def predict_next_actions(self, behavior_sequence, top_k=5):
        """
        Predicts most likely next actions for a user
        """
        with torch.no_grad():
            outputs = self.forward(behavior_sequence)
            next_action_probs = torch.softmax(outputs['next_action_logits'][:, -1, :], dim=-1)

            # Get top K predicted actions
            top_k_probs, top_k_indices = torch.topk(next_action_probs, k=top_k)

            return {
                'predicted_actions': top_k_indices.cpu().numpy(),
                'action_probabilities': top_k_probs.cpu().numpy(),
                'engagement_score': outputs['engagement_scores'][:, -1, :].cpu().numpy(),
                'predicted_intent': torch.argmax(outputs['intent_logits'], dim=-1).cpu().numpy()
            }

class DynamicContentOptimizer:
    def __init__(self):
        self.content_templates = self.load_content_templates()
        self.generation_models = self.load_generation_models()
        self.optimization_history = {}

    async def optimize_content(self, user_profile: Dict, context: Dict) -> Dict:
        """
        Generates optimized content for specific user and context
        """
        # Analyze content requirements
        content_requirements = self.analyze_content_requirements(user_profile, context)

        # Generate content variations
        content_variations = await self.generate_content_variations(content_requirements)

        # Predict performance for each variation
        performance_predictions = self.predict_content_performance(
            content_variations,
            user_profile,
            context
        )

        # Select optimal content
        optimal_content = self.select_optimal_content(
            content_variations,
            performance_predictions
        )

        return {
            'content': optimal_content,
            'predicted_engagement': performance_predictions[optimal_content['id']],
            'optimization_rationale': self.generate_rationale(
                user_profile, context, optimal_content
            )
        }

    def analyze_content_requirements(self, user_profile: Dict, context: Dict) -> Dict:
        """
        Analyzes what type of content would be most effective
        """
        requirements = {}

        # Content complexity based on user sophistication
        if user_profile.get('expertise_level', 'beginner') == 'expert':
            requirements['complexity'] = 'high'
            requirements['detail_level'] = 'comprehensive'
        else:
            requirements['complexity'] = 'medium'
            requirements['detail_level'] = 'accessible'

        # Content format based on context
        if context.get('device_type') == 'mobile':
            requirements['format'] = 'mobile_optimized'
            requirements['length'] = 'concise'
        else:
            requirements['format'] = 'full_featured'
            requirements['length'] = 'detailed'

        # Emotional tone based on user state
        if context.get('user_stress_level', 'normal') == 'high':
            requirements['tone'] = 'reassuring'
            requirements['approach'] = 'solution_focused'
        else:
            requirements['tone'] = 'engaging'
            requirements['approach'] = 'exploratory'

        # Content topics based on interests and intent
        requirements['primary_topics'] = user_profile.get('interests', [])
        requirements['content_intent'] = context.get('predicted_intent', 'informational')

        return requirements

    async def generate_content_variations(self, requirements: Dict) -> List[Dict]:
        """
        Generates multiple content variations based on requirements
        """
        variations = []

        # Generate headline variations
        headlines = await self.generate_headlines(requirements)

        # Generate body content variations
        for headline in headlines:
            body_variations = await self.generate_body_content(headline, requirements)

            for body in body_variations:
                # Generate call-to-action variations
                cta_variations = self.generate_cta_variations(requirements)

                for cta in cta_variations:
                    variation = {
                        'id': f"content_{len(variations)}",
                        'headline': headline,
                        'body': body,
                        'cta': cta,
                        'requirements': requirements
                    }
                    variations.append(variation)

        return variations[:20]  # Limit to top 20 variations

    def predict_content_performance(self, content_variations: List[Dict],
                                  user_profile: Dict, context: Dict) -> Dict:
        """
        Predicts engagement performance for each content variation
        """
        predictions = {}

        for variation in content_variations:
            # Extract content features
            content_features = self.extract_content_features(variation)

            # Combine with user and context features
            combined_features = {
                **content_features,
                **self.extract_user_features(user_profile),
                **self.extract_context_features(context)
            }

            # Predict engagement metrics
            prediction = self.generation_models['performance_predictor'].predict(
                combined_features
            )

            predictions[variation['id']] = {
                'engagement_score': prediction['engagement_score'],
                'click_through_rate': prediction['ctr'],
                'conversion_probability': prediction['conversion_prob'],
                'time_on_content': prediction['time_on_content']
            }

        return predictions

class VisualPersonalizationEngine:
    def __init__(self):
        self.image_models = self.load_image_models()
        self.layout_optimizer = LayoutOptimizer()
        self.color_analyzer = ColorPreferenceAnalyzer()

    async def optimize_visual_content(self, user_profile: Dict, content_context: Dict) -> Dict:
        """
        Optimizes visual elements for individual users
        """
        optimizations = {}

        # Image selection and optimization
        optimizations['images'] = await self.optimize_images(user_profile, content_context)

        # Layout optimization
        optimizations['layout'] = await self.optimize_layout(user_profile, content_context)

        # Color scheme optimization
        optimizations['colors'] = await self.optimize_colors(user_profile, content_context)

        # Typography optimization
        optimizations['typography'] = await self.optimize_typography(user_profile, content_context)

        return optimizations

    async def optimize_images(self, user_profile: Dict, content_context: Dict) -> Dict:
        """
        Selects and optimizes images based on user preferences
        """
        # Analyze user's image preferences from historical data
        image_preferences = self.analyze_image_preferences(user_profile)

        # Get available images for content
        available_images = content_context.get('available_images', [])

        # Score images based on user preferences
        image_scores = {}
        for image in available_images:
            image_features = self.extract_image_features(image)
            score = self.calculate_image_preference_score(image_features, image_preferences)
            image_scores[image['id']] = score

        # Select optimal images
        optimal_images = sorted(
            available_images,
            key=lambda x: image_scores[x['id']],
            reverse=True
        )[:5]

        return {
            'recommended_images': optimal_images,
            'image_scores': image_scores,
            'preference_insights': image_preferences
        }

    def analyze_image_preferences(self, user_profile: Dict) -> Dict:
        """
        Analyzes user's image preferences from behavior data
        """
        image_interactions = user_profile.get('image_interactions', [])

        preferences = {
            'preferred_styles': defaultdict(float),
            'preferred_colors': defaultdict(float),
            'preferred_subjects': defaultdict(float),
            'preferred_compositions': defaultdict(float)
        }

        for interaction in image_interactions:
            if interaction.get('engagement_score', 0) > 0.7:  # High engagement
                image_metadata = interaction.get('image_metadata', {})

                # Accumulate style preferences
                style = image_metadata.get('style', 'unknown')
                preferences['preferred_styles'][style] += interaction['engagement_score']

                # Accumulate color preferences
                dominant_colors = image_metadata.get('dominant_colors', [])
                for color in dominant_colors:
                    preferences['preferred_colors'][color] += interaction['engagement_score']

                # Accumulate subject preferences
                subjects = image_metadata.get('subjects', [])
                for subject in subjects:
                    preferences['preferred_subjects'][subject] += interaction['engagement_score']

        # Normalize preferences
        for pref_type in preferences:
            total = sum(preferences[pref_type].values())
            if total > 0:
                for key in preferences[pref_type]:
                    preferences[pref_type][key] /= total

        return preferences

class BehavioralSegmentationEngine:
    def __init__(self):
        self.clustering_models = self.load_clustering_models()
        self.behavioral_analyzers = self.load_behavioral_analyzers()

    def create_behavioral_segments(self, user_data: List[Dict]) -> Dict:
        """
        Creates behavioral segments from user data
        """
        # Extract behavioral features
        behavioral_features = self.extract_behavioral_features(user_data)

        # Apply clustering algorithms
        segments = {}

        # K-means clustering for broad behavioral groups
        kmeans_segments = self.clustering_models['kmeans'].fit_predict(behavioral_features)
        segments['broad_segments'] = self.interpret_kmeans_segments(kmeans_segments, user_data)

        # Hierarchical clustering for detailed segmentation
        hierarchical_segments = self.clustering_models['hierarchical'].fit_predict(behavioral_features)
        segments['detailed_segments'] = self.interpret_hierarchical_segments(
            hierarchical_segments, user_data
        )

        # Time-based behavioral clustering
        temporal_segments = self.create_temporal_segments(user_data)
        segments['temporal_segments'] = temporal_segments

        return segments

    def extract_behavioral_features(self, user_data: List[Dict]) -> np.ndarray:
        """
        Extracts comprehensive behavioral features for clustering
        """
        features_list = []

        for user in user_data:
            user_features = []

            # Engagement patterns
            user_features.append(user.get('avg_session_duration', 0))
            user_features.append(user.get('pages_per_session', 0))
            user_features.append(user.get('bounce_rate', 0))
            user_features.append(user.get('return_visit_rate', 0))

            # Content consumption patterns
            user_features.append(user.get('content_depth_score', 0))
            user_features.append(user.get('video_completion_rate', 0))
            user_features.append(user.get('reading_speed', 0))
            user_features.append(user.get('social_sharing_rate', 0))

            # Conversion behavior
            user_features.append(user.get('conversion_rate', 0))
            user_features.append(user.get('cart_abandonment_rate', 0))
            user_features.append(user.get('purchase_frequency', 0))
            user_features.append(user.get('avg_order_value', 0))

            # Search and discovery behavior
            user_features.append(user.get('search_usage_rate', 0))
            user_features.append(user.get('filter_usage_rate', 0))
            user_features.append(user.get('category_exploration_score', 0))

            # Temporal patterns
            user_features.extend(self.extract_temporal_features(user))

            features_list.append(user_features)

        return np.array(features_list)

    def create_personalized_targeting_strategy(self, user_id: str, segments: Dict) -> Dict:
        """
        Creates personalized targeting strategy based on behavioral segments
        """
        user_segments = self.get_user_segments(user_id, segments)

        strategy = {
            'primary_segment': user_segments['primary'],
            'secondary_segments': user_segments['secondary'],
            'targeting_tactics': {},
            'personalization_priorities': {},
            'engagement_optimization': {}
        }

        # Define targeting tactics based on segments
        if user_segments['primary'] == 'high_engagement_converters':
            strategy['targeting_tactics'] = {
                'content_type': 'premium_detailed',
                'messaging_tone': 'exclusive_insider',
                'offer_strategy': 'early_access_premium',
                'communication_frequency': 'high'
            }

        elif user_segments['primary'] == 'browsers_researchers':
            strategy['targeting_tactics'] = {
                'content_type': 'educational_comparative',
                'messaging_tone': 'helpful_informative',
                'offer_strategy': 'value_demonstration',
                'communication_frequency': 'medium'
            }

        elif user_segments['primary'] == 'quick_decision_makers':
            strategy['targeting_tactics'] = {
                'content_type': 'concise_action_oriented',
                'messaging_tone': 'urgent_direct',
                'offer_strategy': 'limited_time_clear_value',
                'communication_frequency': 'targeted_high_impact'
            }

        # Define personalization priorities
        strategy['personalization_priorities'] = {
            'content_ranking': self.get_content_ranking_strategy(user_segments),
            'product_recommendations': self.get_recommendation_strategy(user_segments),
            'ui_adaptation': self.get_ui_adaptation_strategy(user_segments),
            'timing_optimization': self.get_timing_strategy(user_segments)
        }

        return strategy

class IntentBasedPersonalization:
    def __init__(self):
        self.intent_models = self.load_intent_models()
        self.personalization_engines = self.load_personalization_engines()

    async def detect_and_respond_to_intent(self, user_session: Dict) -> Dict:
        """
        Detects user intent and personalizes experience accordingly
        """
        # Analyze current session for intent signals
        intent_signals = self.extract_intent_signals(user_session)

        # Predict user intent using ML models
        predicted_intent = await self.predict_user_intent(intent_signals)

        # Generate intent-specific personalization
        personalization_response = await self.generate_intent_response(
            predicted_intent,
            user_session
        )

        return {
            'detected_intent': predicted_intent,
            'personalization_response': personalization_response,
            'confidence_score': predicted_intent['confidence'],
            'recommended_actions': personalization_response['recommended_actions']
        }

    def extract_intent_signals(self, user_session: Dict) -> Dict:
        """
        Extracts signals that indicate user intent
        """
        signals = {}

        # Navigation patterns
        page_sequence = user_session.get('page_sequence', [])
        signals['navigation_pattern'] = self.analyze_navigation_pattern(page_sequence)

        # Search behavior
        search_queries = user_session.get('search_queries', [])
        signals['search_intent'] = self.analyze_search_intent(search_queries)

        # Interaction patterns
        interactions = user_session.get('interactions', [])
        signals['interaction_intent'] = self.analyze_interaction_patterns(interactions)

        # Content engagement
        content_interactions = user_session.get('content_interactions', [])
        signals['content_intent'] = self.analyze_content_engagement(content_interactions)

        # Time-based signals
        signals['urgency_indicators'] = self.analyze_urgency_signals(user_session)

        # Referral context
        signals['referral_intent'] = self.analyze_referral_context(user_session)

        return signals

    async def predict_user_intent(self, intent_signals: Dict) -> Dict:
        """
        Predicts user intent using ensemble of ML models
        """
        # Primary intent classification
        primary_intent = self.intent_models['primary_classifier'].predict(intent_signals)

        # Intent confidence scoring
        confidence_score = self.intent_models['confidence_scorer'].predict(intent_signals)

        # Sub-intent classification
        sub_intent = self.intent_models['sub_intent_classifier'].predict(
            intent_signals, primary_intent
        )

        # Intent progression prediction
        intent_progression = self.intent_models['progression_predictor'].predict(
            intent_signals, primary_intent
        )

        return {
            'primary_intent': primary_intent,
            'sub_intent': sub_intent,
            'confidence': confidence_score,
            'intent_progression': intent_progression,
            'intent_strength': self.calculate_intent_strength(intent_signals),
            'time_sensitivity': self.calculate_time_sensitivity(intent_signals)
        }

    async def generate_intent_response(self, predicted_intent: Dict, user_session: Dict) -> Dict:
        """
        Generates personalized response based on predicted intent
        """
        response = {}

        intent_type = predicted_intent['primary_intent']

        if intent_type == 'purchase_intent':
            response = await self.generate_purchase_intent_response(predicted_intent, user_session)
        elif intent_type == 'research_intent':
            response = await self.generate_research_intent_response(predicted_intent, user_session)
        elif intent_type == 'support_intent':
            response = await self.generate_support_intent_response(predicted_intent, user_session)
        elif intent_type == 'exploration_intent':
            response = await self.generate_exploration_intent_response(predicted_intent, user_session)

        # Add confidence-based adaptations
        if predicted_intent['confidence'] < 0.7:
            response['adaptation_strategy'] = 'gradual_personalization'
        else:
            response['adaptation_strategy'] = 'aggressive_personalization'

        return response

    async def generate_purchase_intent_response(self, intent: Dict, session: Dict) -> Dict:
        """
        Generates response optimized for purchase intent
        """
        return {
            'content_priority': 'product_focused',
            'ui_adaptations': {
                'emphasize_cta': True,
                'show_urgency_indicators': True,
                'highlight_value_proposition': True,
                'simplify_checkout_flow': True
            },
            'content_adaptations': {
                'show_product_benefits': True,
                'include_social_proof': True,
                'display_limited_offers': True,
                'surface_related_products': True
            },
            'messaging_strategy': {
                'tone': 'confident_solution_oriented',
                'urgency_level': 'medium_high',
                'value_emphasis': 'roi_focused'
            },
            'recommended_actions': [
                'show_personalized_offers',
                'display_recently_viewed_products',
                'enable_quick_purchase_options',
                'provide_purchase_assistance'
            ]
        }