""" Contextual Personalization & User Profiling System =============================================== Advanced user profiling system that builds user-specific contextual profiles, continuously updates them based on interactions, and enables behavioral adaptation. """ import asyncio import json import logging from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Set, Tuple, Union from dataclasses import dataclass, field, asdict from enum import Enum import numpy as np from collections import defaultdict, deque import hashlib from sklearn.metrics.pairwise import cosine_similarity from sklearn.cluster import DBSCAN from sklearn.decomposition import PCA from ai_agent_framework.core.context_engineering_agent import ( ContextElement, ContextModality, ContextDimension, ContextEngineeringAgent ) logger = logging.getLogger(__name__) class ProfileType(Enum): """Types of user profiles.""" BEHAVIORAL = "behavioral" PREFERENTIAL = "preferential" CONTEXTUAL = "contextual" INTERACTION = "interaction" LEARNING = "learning" COLLABORATIVE = "collaborative" TEMPORAL = "temporal" class LearningType(Enum): """Types of learning patterns.""" GRADUAL = "gradual" RAPID = "rapid" CYCLICAL = "cyclical" EVENT_DRIVEN = "event_driven" ADAPTIVE = "adaptive" @dataclass class UserInteraction: """Represents a user interaction with the system.""" interaction_id: str user_id: str interaction_type: str content: Dict[str, Any] context: Dict[str, Any] timestamp: datetime duration: float success: bool satisfaction_score: Optional[float] = None adaptation_needed: bool = False def __post_init__(self): if not self.interaction_id: self.interaction_id = f"interaction_{int(time.time())}_{hash(str(self.content))}" if not self.timestamp: self.timestamp = datetime.utcnow() @dataclass class UserPreference: """Represents a user preference.""" preference_id: str user_id: str category: str preference_type: str value: Any confidence: float stability: float last_updated: datetime evidence_count: int def __post_init__(self): if not self.preference_id: self.preference_id = f"pref_{self.user_id}_{self.category}_{hash(str(self.value))}" if not self.last_updated: self.last_updated = datetime.utcnow() @dataclass class ContextualPattern: """Represents a contextual usage pattern.""" pattern_id: str user_id: str pattern_type: str context_elements: Set[str] frequency: float success_rate: float last_observed: datetime confidence: float def __post_init__(self): if not self.pattern_id: self.pattern_id = f"pattern_{self.user_id}_{self.pattern_type}_{int(time.time())}" @dataclass class UserProfile: """Comprehensive user profile for contextual personalization.""" user_id: str profile_type: ProfileType data: Dict[str, Any] created_at: datetime updated_at: datetime version: int completeness_score: float confidence_score: float def __post_init__(self): if not self.created_at: self.created_at = datetime.utcnow() if not self.updated_at: self.updated_at = self.created_at if self.version == 0: self.version = 1 class ContextualPersonalizationEngine: """Core engine for contextual personalization and user profiling.""" def __init__(self): self.user_profiles = {} # user_id -> dict of profile_type -> UserProfile self.interaction_history = {} # user_id -> List[UserInteraction] self.preference_database = {} # user_id -> List[UserPreference] self.pattern_database = {} # user_id -> List[ContextualPattern] self.profile_weights = { ProfileType.BEHAVIORAL: 0.25, ProfileType.PREFERENTIAL: 0.20, ProfileType.CONTEXTUAL: 0.20, ProfileType.INTERACTION: 0.15, ProfileType.LEARNING: 0.10, ProfileType.COLLABORATIVE: 0.05, ProfileType.TEMPORAL: 0.05 } self.learning_algorithms = { "incremental": self._incremental_learning, "batch": self._batch_learning, "reinforcement": self._reinforcement_learning, "association": self._association_learning } self.adaptation_strategies = { "gradual": self._gradual_adaptation, "immediate": self._immediate_adaptation, "predictive": self._predictive_adaptation } async def process_user_interaction(self, interaction: UserInteraction) -> Dict[str, Any]: """Process a new user interaction and update profiles.""" try: # Step 1: Extract insights from interaction insights = await self._extract_interaction_insights(interaction) # Step 2: Update relevant profiles updated_profiles = await self._update_profiles_from_interaction(interaction, insights) # Step 3: Identify new patterns new_patterns = await self._identify_contextual_patterns(interaction, insights) # Step 4: Update preferences updated_preferences = await self._update_preferences(interaction, insights) # Step 5: Generate adaptation recommendations adaptations = await self._generate_adaptation_recommendations( interaction, updated_profiles, insights ) return { "interaction_id": interaction.interaction_id, "insights": insights, "updated_profiles": updated_profiles, "new_patterns": [asdict(pattern) for pattern in new_patterns], "updated_preferences": updated_preferences, "adaptation_recommendations": adaptations, "processing_success": True } except Exception as e: logger.error(f"Failed to process user interaction: {e}") return { "interaction_id": interaction.interaction_id, "error": str(e), "processing_success": False } async def build_user_profile( self, user_id: str, profile_type: ProfileType, include_interaction_history: bool = True ) -> UserProfile: """Build a comprehensive user profile.""" # Get available data for the user interaction_data = self.interaction_history.get(user_id, []) preference_data = self.preference_database.get(user_id, []) pattern_data = self.pattern_database.get(user_id, []) if not interaction_data and not preference_data: return await self._create_empty_profile(user_id, profile_type) # Build profile based on type if profile_type == ProfileType.BEHAVIORAL: return await self._build_behavioral_profile(user_id, interaction_data, preference_data) elif profile_type == ProfileType.PREFERENTIAL: return await self._build_preferential_profile(user_id, preference_data, interaction_data) elif profile_type == ProfileType.CONTEXTUAL: return await self._build_contextual_profile(user_id, interaction_data, pattern_data) elif profile_type == ProfileType.INTERACTION: return await self._build_interaction_profile(user_id, interaction_data) elif profile_type == ProfileType.LEARNING: return await self._build_learning_profile(user_id, interaction_data) elif profile_type == ProfileType.COLLABORATIVE: return await self._build_collaborative_profile(user_id, interaction_data, pattern_data) elif profile_type == ProfileType.TEMPORAL: return await self._build_temporal_profile(user_id, interaction_data, pattern_data) else: return await self._build_generic_profile(user_id, profile_type, interaction_data) async def update_cross_session_context( self, user_id: str, current_context: Dict[str, Any], session_duration: float ) -> Dict[str, Any]: """Update and maintain cross-session context continuity.""" # Get or create user profile profile = await self.get_user_profile(user_id, ProfileType.CONTEXTUAL) # Extract current session context session_context = { "session_start": datetime.utcnow(), "session_duration": session_duration, "context_elements": current_context, "session_type": self._classify_session_type(current_context) } # Update persistent context persistent_context = await self._update_persistent_context( profile.data.get("persistent_context", {}), session_context ) # Identify context patterns across sessions context_patterns = await self._identify_cross_session_patterns( user_id, persistent_context, session_context ) # Generate continuity recommendations continuity_recommendations = await self._generate_continuity_recommendations( user_id, persistent_context, context_patterns ) # Update profile updated_data = profile.data.copy() updated_data.update({ "persistent_context": persistent_context, "cross_session_patterns": context_patterns, "last_session": session_context, "continuity_strength": self._calculate_continuity_strength(context_patterns) }) updated_profile = UserProfile( user_id=user_id, profile_type=ProfileType.CONTEXTUAL, data=updated_data, created_at=profile.created_at, updated_at=datetime.utcnow(), version=profile.version + 1, completeness_score=self._calculate_profile_completeness(updated_data), confidence_score=self._calculate_profile_confidence(updated_data) ) # Store updated profile await self._store_user_profile(updated_profile) return { "persistent_context": persistent_context, "context_patterns": context_patterns, "continuity_recommendations": continuity_recommendations, "continuity_strength": updated_profile.data.get("continuity_strength", 0.0) } async def generate_personalized_adaptation( self, user_id: str, current_context: Dict[str, Any], adaptation_type: str = "gradual" ) -> Dict[str, Any]: """Generate personalized adaptation based on user profile.""" # Get relevant profiles profiles = await self._get_user_profiles(user_id) # Extract user characteristics user_characteristics = await self._extract_user_characteristics(profiles) # Analyze current context context_analysis = await self._analyze_current_context(current_context, user_characteristics) # Generate adaptation strategy adaptation_strategy = await self.adaptation_strategies.get( adaptation_type, self._gradual_adaptation )(user_id, context_analysis, user_characteristics) # Validate adaptation validated_adaptation = await self._validate_adaptation( adaptation_strategy, user_characteristics, current_context ) return validated_adaptation async def _extract_interaction_insights(self, interaction: UserInteraction) -> Dict[str, Any]: """Extract insights from user interaction.""" insights = { "interaction_complexity": self._calculate_interaction_complexity(interaction), "user_engagement_level": self._calculate_engagement_level(interaction), "context_sensitivity": self._calculate_context_sensitivity(interaction), "learning_velocity": self._calculate_learning_velocity(interaction), "preference_signals": self._extract_preference_signals(interaction), "behavioral_patterns": self._extract_behavioral_patterns(interaction), "success_factors": self._identify_success_factors(interaction), "adaptation_triggers": self._identify_adaptation_triggers(interaction) } return insights def _calculate_interaction_complexity(self, interaction: UserInteraction) -> float: """Calculate interaction complexity score.""" content_complexity = 0.0 context_complexity = 0.0 duration_factor = min(1.0, interaction.duration / 3600) # Normalize by hour # Content complexity content_size = len(str(interaction.content)) if content_size > 10000: content_complexity = 1.0 elif content_size > 1000: content_complexity = 0.7 elif content_size > 100: content_complexity = 0.4 else: content_complexity = 0.2 # Context complexity context_size = len(str(interaction.context)) if context_size > 5000: context_complexity = 1.0 elif context_size > 500: context_complexity = 0.7 elif context_size > 50: context_complexity = 0.4 else: context_complexity = 0.1 # Overall complexity complexity = (content_complexity + context_complexity) * 0.4 + duration_factor * 0.2 return min(1.0, complexity) def _calculate_engagement_level(self, interaction: UserInteraction) -> float: """Calculate user engagement level.""" # Factors: duration, success, interaction types, context richness duration_score = min(1.0, interaction.duration / 1800) # 30 minutes max success_score = 1.0 if interaction.success else 0.3 interaction_type_diversity = len(set( interaction.content.get("interaction_types", []) )) / 10 # Normalize context_richness = len(interaction.context) / 20 # Normalize # Weighted combination engagement = ( duration_score * 0.3 + success_score * 0.3 + min(1.0, interaction_type_diversity) * 0.2 + min(1.0, context_richness) * 0.2 ) return min(1.0, engagement) def _calculate_context_sensitivity(self, interaction: UserInteraction) -> float: """Calculate context sensitivity of the interaction.""" # Analyze how much the interaction depends on context context_dependent_elements = 0 total_elements = 0 # Check content for context dependencies content_str = json.dumps(interaction.content) context_markers = ["context", "situation", "environment", "previous", "history"] for marker in context_markers: if marker in content_str.lower(): context_dependent_elements += 1 total_elements += len(context_markers) # Check context richness context_size = len(interaction.context) context_richness = min(1.0, context_size / 50) if total_elements > 0: context_sensitivity = (context_dependent_elements / total_elements) * 0.6 + context_richness * 0.4 else: context_sensitivity = context_richness return min(1.0, context_sensitivity) def _calculate_learning_velocity(self, interaction: UserInteraction) -> float: """Calculate learning velocity based on interaction patterns.""" # This would need historical data to calculate properly # For now, use a simplified calculation # Learning indicators time_taken = interaction.duration success = interaction.success adaptation_needed = interaction.adaptation_needed # Velocity calculation if time_taken < 300: # 5 minutes velocity = 1.0 if success else 0.7 elif time_taken < 900: # 15 minutes velocity = 0.8 if success else 0.5 else: velocity = 0.6 if success else 0.3 # Adjust for adaptation need if adaptation_needed: velocity *= 0.8 return min(1.0, velocity) def _extract_preference_signals(self, interaction: UserInteraction) -> List[Dict[str, Any]]: """Extract preference signals from interaction.""" signals = [] content = interaction.content context = interaction.context # Look for explicit preferences if "preferences" in content: for pref_category, pref_value in content["preferences"].items(): signals.append({ "type": "explicit", "category": pref_category, "value": pref_value, "confidence": 0.9, "timestamp": interaction.timestamp }) # Look for implicit preferences if "choices" in content: for choice in content["choices"]: signals.append({ "type": "implicit", "category": choice.get("category", "unknown"), "value": choice.get("selected", choice.get("value")), "confidence": 0.7, "timestamp": interaction.timestamp }) # Look for interaction style preferences if "interaction_style" in context: signals.append({ "type": "behavioral", "category": "interaction_style", "value": context["interaction_style"], "confidence": 0.8, "timestamp": interaction.timestamp }) return signals def _extract_behavioral_patterns(self, interaction: UserInteraction) -> List[Dict[str, Any]]: """Extract behavioral patterns from interaction.""" patterns = [] # Time patterns if "timestamp" in interaction.context: hour = interaction.timestamp.hour patterns.append({ "type": "temporal", "pattern": f"active_at_hour_{hour}", "strength": 1.0, "context": {"hour": hour} }) # Interaction style patterns if "interaction_type" in interaction.content: patterns.append({ "type": "interaction_style", "pattern": f"prefers_{interaction.content['interaction_type']}", "strength": 0.8, "context": interaction.content["interaction_type"] }) # Success pattern if interaction.success: patterns.append({ "type": "success_pattern", "pattern": "successful_interaction", "strength": 1.0, "context": {"duration": interaction.duration} }) return patterns def _identify_success_factors(self, interaction: UserInteraction) -> Dict[str, float]: """Identify factors that contribute to interaction success.""" factors = {} # Duration factor optimal_duration = 600 # 10 minutes duration_ratio = 1.0 - abs(interaction.duration - optimal_duration) / optimal_duration factors["duration_optimization"] = max(0.0, duration_ratio) # Context factor context_richness = min(1.0, len(interaction.context) / 20) factors["context_richness"] = context_richness # Engagement factor engagement = self._calculate_engagement_level(interaction) factors["engagement_level"] = engagement return factors def _identify_adaptation_triggers(self, interaction: UserInteraction) -> List[Dict[str, Any]]: """Identify triggers that would necessitate adaptation.""" triggers = [] # Performance triggers if not interaction.success: triggers.append({ "type": "performance", "trigger": "interaction_failed", "severity": 0.8, "timestamp": interaction.timestamp }) # Engagement triggers engagement = self._calculate_engagement_level(interaction) if engagement < 0.5: triggers.append({ "type": "engagement", "trigger": "low_engagement", "severity": 0.6, "context": {"engagement": engagement} }) # Duration triggers if interaction.duration > 3600: # 1 hour triggers.append({ "type": "duration", "trigger": "prolonged_interaction", "severity": 0.4, "context": {"duration": interaction.duration} }) return triggers # Profile building methods async def _build_behavioral_profile( self, user_id: str, interactions: List[UserInteraction], preferences: List[UserPreference] ) -> UserProfile: """Build behavioral profile for user.""" behavioral_data = { "interaction_patterns": {}, "success_patterns": {}, "preference_stability": {}, "adaptation_frequency": 0.0, "learning_style": "unknown", "communication_style": "unknown", "problem_solving_approach": "unknown" } # Analyze interaction patterns interaction_times = [interaction.timestamp for interaction in interactions] if interaction_times: # Time patterns hours = [interaction.timestamp.hour for interaction in interactions] behavioral_data["time_preferences"] = { "peak_hours": self._find_peak_hours(hours), "session_duration_pattern": self._analyze_duration_patterns(interactions) } # Analyze success patterns successful_interactions = [i for i in interactions if i.success] if interactions: success_rate = len(successful_interactions) / len(interactions) behavioral_data["success_metrics"] = { "overall_success_rate": success_rate, "average_session_duration": np.mean([i.duration for i in interactions]), "adaptation_frequency": np.mean([i.adaptation_needed for i in interactions]) } # Communication style analysis communication_patterns = self._analyze_communication_style(interactions) behavioral_data.update(communication_patterns) # Learning style analysis learning_style = self._determine_learning_style(interactions) behavioral_data["learning_style"] = learning_style return UserProfile( user_id=user_id, profile_type=ProfileType.BEHAVIORAL, data=behavioral_data, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), version=1, completeness_score=self._calculate_profile_completeness(behavioral_data), confidence_score=self._calculate_behavioral_confidence(behavioral_data) ) async def _build_preferential_profile( self, user_id: str, preferences: List[UserPreference], interactions: List[UserInteraction] ) -> UserProfile: """Build preferential profile for user.""" pref_data = { "explicit_preferences": {}, "implicit_preferences": {}, "preference_confidence": {}, "preference_stability": {}, "conflict_resolution": "unknown", "adaptation_to_new": "unknown" } # Process explicit preferences for pref in preferences: if pref.confidence > 0.7: # High confidence preferences if pref.category not in pref_data["explicit_preferences"]: pref_data["explicit_preferences"][pref.category] = {} pref_data["explicit_preferences"][pref.category][pref.preference_type] = { "value": pref.value, "confidence": pref.confidence, "stability": pref.stability } # Process implicit preferences from interactions implicit_prefs = self._extract_implicit_preferences(interactions) pref_data["implicit_preferences"] = implicit_prefs # Calculate preference metrics if preferences: avg_confidence = np.mean([p.confidence for p in preferences]) avg_stability = np.mean([p.stability for p in preferences]) pref_data["preference_confidence"]["average"] = avg_confidence pref_data["preference_stability"]["average"] = avg_stability return UserProfile( user_id=user_id, profile_type=ProfileType.PREFERENTIAL, data=pref_data, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), version=1, completeness_score=self._calculate_profile_completeness(pref_data), confidence_score=self._calculate_preference_confidence(pref_data) ) async def _build_contextual_profile( self, user_id: str, interactions: List[UserInteraction], patterns: List[ContextualPattern] ) -> UserProfile: """Build contextual profile for user.""" context_data = { "frequent_contexts": {}, "context_transitions": {}, "context_sensitivity": 0.0, "persistent_context": {}, "cross_session_patterns": [], "context_evolution": {} } # Analyze frequent contexts all_contexts = [] for interaction in interactions: all_contexts.append(interaction.context) if all_contexts: # Find common context elements common_elements = self._find_common_context_elements(all_contexts) context_data["frequent_contexts"] = common_elements # Analyze context sensitivity context_data["context_sensitivity"] = self._calculate_overall_context_sensitivity(interactions) # Process contextual patterns pattern_analysis = self._analyze_contextual_patterns(patterns) context_data.update(pattern_analysis) return UserProfile( user_id=user_id, profile_type=ProfileType.CONTEXTUAL, data=context_data, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), version=1, completeness_score=self._calculate_profile_completeness(context_data), confidence_score=self._calculate_contextual_confidence(context_data) ) # Helper methods for profile building def _find_peak_hours(self, hours: List[int]) -> Dict[int, int]: """Find peak activity hours.""" hour_counts = defaultdict(int) for hour in hours: hour_counts[hour] += 1 return dict(hour_counts) def _analyze_duration_patterns(self, interactions: List[UserInteraction]) -> Dict[str, float]: """Analyze session duration patterns.""" durations = [interaction.duration for interaction in interactions] return { "average_duration": np.mean(durations), "median_duration": np.median(durations), "std_duration": np.std(durations), "short_sessions": len([d for d in durations if d < 300]), # < 5 min "long_sessions": len([d for d in durations if d > 1800]) # > 30 min } def _analyze_communication_style(self, interactions: List[UserInteraction]) -> Dict[str, Any]: """Analyze user's communication style.""" # Simplified communication style analysis if not interactions: return { "communication_style": "unknown", "verbosity": 0.0, "formality": 0.0 } # Analyze content characteristics total_content = "" for interaction in interactions: total_content += json.dumps(interaction.content) + " " # Simple heuristics for style detection formal_indicators = ["please", "thank you", "would you", "could you"] informal_indicators = ["yeah", "ok", "cool", "awesome"] formal_count = sum(total_content.lower().count(indicator) for indicator in formal_indicators) informal_count = sum(total_content.lower().count(indicator) for indicator in informal_indicators) if formal_count > informal_count: style = "formal" formality_score = min(1.0, formal_count / 10) elif informal_count > formal_count: style = "informal" formality_score = max(0.0, 1.0 - informal_count / 10) else: style = "neutral" formality_score = 0.5 # Verbosity analysis avg_content_size = np.mean([len(json.dumps(i.content)) for i in interactions]) verbosity_score = min(1.0, avg_content_size / 1000) return { "communication_style": style, "formality": formality_score, "verbosity": verbosity_score } def _determine_learning_style(self, interactions: List[UserInteraction]) -> str: """Determine user's learning style based on interaction patterns.""" if not interactions: return "unknown" # Analyze adaptation frequency adaptation_rates = [interaction.adaptation_needed for interaction in interactions] avg_adaptation_rate = np.mean(adaptation_rates) # Analyze success patterns success_patterns = [interaction.success for interaction in interactions] success_improvement = self._calculate_success_improvement(success_patterns) # Learning style determination if avg_adaptation_rate > 0.7: return "adaptive_learner" elif success_improvement > 0.3: return "progressive_learner" elif len(interactions) > 10: return "experienced_user" else: return "new_user" def _calculate_success_improvement(self, success_patterns: List[bool]) -> float: """Calculate improvement in success rate over time.""" if len(success_patterns) < 5: return 0.0 # Split into early and late interactions mid_point = len(success_patterns) // 2 early_success = np.mean(success_patterns[:mid_point]) late_success = np.mean(success_patterns[mid_point:]) return late_success - early_success def _extract_implicit_preferences(self, interactions: List[UserInteraction]) -> Dict[str, Any]: """Extract implicit preferences from interactions.""" implicit_prefs = { "feature_usage": {}, "interaction_patterns": {}, "success_patterns": {} } # Analyze feature usage all_features = [] for interaction in interactions: if "features_used" in interaction.content: all_features.extend(interaction.content["features_used"]) if all_features: from collections import Counter feature_counts = Counter(all_features) total_usage = sum(feature_counts.values()) for feature, count in feature_counts.items(): implicit_prefs["feature_usage"][feature] = count / total_usage return implicit_prefs def _find_common_context_elements(self, contexts: List[Dict[str, Any]]) -> Dict[str, float]: """Find common context elements across interactions.""" if not contexts: return {} # Collect all context keys all_keys = set() for context in contexts: all_keys.update(context.keys()) # Count frequency of each key key_counts = defaultdict(int) for context in contexts: for key in context.keys(): key_counts[key] += 1 # Calculate frequency ratios common_elements = {} for key in all_keys: frequency = key_counts[key] / len(contexts) if frequency > 0.3: # Appears in >30% of contexts common_elements[key] = frequency return common_elements def _calculate_overall_context_sensitivity(self, interactions: List[UserInteraction]) -> float: """Calculate overall context sensitivity.""" if not interactions: return 0.0 sensitivities = [] for interaction in interactions: sensitivity = self._calculate_context_sensitivity(interaction) sensitivities.append(sensitivity) return np.mean(sensitivities) def _analyze_contextual_patterns(self, patterns: List[ContextualPattern]) -> Dict[str, Any]: """Analyze contextual patterns.""" pattern_analysis = { "pattern_count": len(patterns), "pattern_types": {}, "high_frequency_patterns": [], "recent_patterns": [] } # Analyze pattern types type_counts = defaultdict(int) for pattern in patterns: type_counts[pattern.pattern_type] += 1 pattern_analysis["pattern_types"] = dict(type_counts) # High frequency patterns high_freq_patterns = [p for p in patterns if p.frequency > 0.5] pattern_analysis["high_frequency_patterns"] = [ { "type": p.pattern_type, "frequency": p.frequency, "confidence": p.confidence } for p in high_freq_patterns ] # Recent patterns recent_patterns = [ p for p in patterns if (datetime.utcnow() - p.last_observed).days < 7 ] pattern_analysis["recent_patterns"] = [ { "type": p.pattern_type, "last_observed": p.last_observed.isoformat() } for p in recent_patterns ] return pattern_analysis # Profile utility methods def _calculate_profile_completeness(self, data: Dict[str, Any]) -> float: """Calculate completeness score of profile data.""" if not data: return 0.0 # Count filled fields filled_fields = 0 total_fields = 0 def count_fields(obj, path=""): nonlocal filled_fields, total_fields if isinstance(obj, dict): for key, value in obj.items(): new_path = f"{path}.{key}" if path else key count_fields(value, new_path) elif isinstance(obj, list): if obj: count_fields(obj[0], f"{path}[0]") # Sample first item else: total_fields += 1 else: total_fields += 1 if obj is not None and obj != "": filled_fields += 1 count_fields(data) return filled_fields / max(total_fields, 1) def _calculate_behavioral_confidence(self, data: Dict[str, Any]) -> float: """Calculate confidence score for behavioral profile.""" # Base confidence on data richness and consistency confidence_factors = [] if "interaction_patterns" in data and data["interaction_patterns"]: confidence_factors.append(0.8) if "success_patterns" in data and data["success_patterns"]: confidence_factors.append(0.7) if "communication_style" in data and data["communication_style"] != "unknown": confidence_factors.append(0.9) if "learning_style" in data and data["learning_style"] != "unknown": confidence_factors.append(0.8) return np.mean(confidence_factors) if confidence_factors else 0.3 def _calculate_preference_confidence(self, data: Dict[str, Any]) -> float: """Calculate confidence score for preference profile.""" confidence_factors = [] if "explicit_preferences" in data and data["explicit_preferences"]: # High confidence for explicit preferences num_explicit = sum(len(category) for category in data["explicit_preferences"].values()) confidence_factors.append(min(1.0, num_explicit / 10)) if "implicit_preferences" in data and data["implicit_preferences"]: confidence_factors.append(0.6) # Lower for implicit if "preference_confidence" in data: avg_confidence = data["preference_confidence"].get("average", 0) confidence_factors.append(avg_confidence) return np.mean(confidence_factors) if confidence_factors else 0.2 def _calculate_contextual_confidence(self, data: Dict[str, Any]) -> float: """Calculate confidence score for contextual profile.""" confidence_factors = [] if "frequent_contexts" in data and data["frequent_contexts"]: confidence_factors.append(0.8) if "cross_session_patterns" in data and data["cross_session_patterns"]: confidence_factors.append(0.9) if "context_sensitivity" in data and data["context_sensitivity"] > 0: confidence_factors.append(data["context_sensitivity"]) return np.mean(confidence_factors) if confidence_factors else 0.3 async def get_user_profile(self, user_id: str, profile_type: ProfileType) -> UserProfile: """Get user profile by type.""" if user_id not in self.user_profiles: return await self._create_empty_profile(user_id, profile_type) user_profiles = self.user_profiles[user_id] if profile_type not in user_profiles: return await self._build_user_profile(user_id, profile_type) return user_profiles[profile_type] async def _create_empty_profile(self, user_id: str, profile_type: ProfileType) -> UserProfile: """Create an empty profile.""" return UserProfile( user_id=user_id, profile_type=profile_type, data={}, created_at=datetime.utcnow(), updated_at=datetime.utcnow(), version=1, completeness_score=0.0, confidence_score=0.0 ) # Placeholder methods for remaining functionality # (These would be fully implemented in a production system) async def _update_profiles_from_interaction(self, interaction: UserInteraction, insights: Dict[str, Any]) -> Dict[str, str]: """Update profiles based on interaction.""" # Implementation would update relevant profiles return {"status": "updated_profiles"} async def _identify_contextual_patterns(self, interaction: UserInteraction, insights: Dict[str, Any]) -> List[ContextualPattern]: """Identify new contextual patterns.""" # Implementation would identify patterns return [] async def _update_preferences(self, interaction: UserInteraction, insights: Dict[str, Any]) -> Dict[str, Any]: """Update user preferences.""" # Implementation would update preferences return {"status": "updated_preferences"} async def _generate_adaptation_recommendations(self, interaction: UserInteraction, profiles: Dict[str, Any], insights: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate adaptation recommendations.""" # Implementation would generate recommendations return [] async def _build_interaction_profile(self, user_id: str, interactions: List[UserInteraction]) -> UserProfile: """Build interaction profile.""" return await self._create_empty_profile(user_id, ProfileType.INTERACTION) async def _build_learning_profile(self, user_id: str, interactions: List[UserInteraction]) -> UserProfile: """Build learning profile.""" return await self._create_empty_profile(user_id, ProfileType.LEARNING) async def _build_collaborative_profile(self, user_id: str, interactions: List[UserInteraction], patterns: List[ContextualPattern]) -> UserProfile: """Build collaborative profile.""" return await self._create_empty_profile(user_id, ProfileType.COLLABORATIVE) async def _build_temporal_profile(self, user_id: str, interactions: List[UserInteraction], patterns: List[ContextualPattern]) -> UserProfile: """Build temporal profile.""" return await self._create_empty_profile(user_id, ProfileType.TEMPORAL) async def _build_generic_profile(self, user_id: str, profile_type: ProfileType, interactions: List[UserInteraction]) -> UserProfile: """Build generic profile.""" return await self._create_empty_profile(user_id, profile_type) async def _store_user_profile(self, profile: UserProfile) -> None: """Store user profile.""" if profile.user_id not in self.user_profiles: self.user_profiles[profile.user_id] = {} self.user_profiles[profile.user_id][profile.profile_type] = profile async def _get_user_profiles(self, user_id: str) -> Dict[ProfileType, UserProfile]: """Get all profiles for a user.""" return self.user_profiles.get(user_id, {}) async def _extract_user_characteristics(self, profiles: Dict[ProfileType, UserProfile]) -> Dict[str, Any]: """Extract user characteristics from profiles.""" characteristics = {} for profile_type, profile in profiles.items(): weight = self.profile_weights.get(profile_type, 0.1) characteristics[profile_type.value] = { "data": profile.data, "confidence": profile.confidence_score, "weight": weight } return characteristics async def _analyze_current_context(self, context: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]: """Analyze current context in relation to user characteristics.""" # Simplified analysis return { "context_relevance": 0.8, "adaptation_need": 0.5, "personalization_opportunities": ["style", "preferences"] } # Adaptation strategy methods async def _gradual_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]: """Gradual adaptation strategy.""" return { "strategy": "gradual", "adaptation_rate": 0.1, "target_aspects": ["communication_style", "interaction_pace"], "timeline": "multiple_sessions" } async def _immediate_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]: """Immediate adaptation strategy.""" return { "strategy": "immediate", "adaptation_rate": 0.8, "target_aspects": ["user_preferences", "interface_layout"], "timeline": "current_session" } async def _predictive_adaptation(self, user_id: str, context_analysis: Dict[str, Any], characteristics: Dict[str, Any]) -> Dict[str, Any]: """Predictive adaptation strategy.""" return { "strategy": "predictive", "adaptation_rate": 0.3, "target_aspects": ["upcoming_needs", "anticipated_preferences"], "timeline": "future_sessions" } async def _validate_adaptation(self, adaptation: Dict[str, Any], characteristics: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Validate adaptation strategy.""" # Simplified validation validated_adaptation = adaptation.copy() validated_adaptation["validation_passed"] = True validated_adaptation["confidence"] = 0.8 return validated_adaptation # Context continuity methods def _classify_session_type(self, context: Dict[str, Any]) -> str: """Classify the type of session based on context.""" # Simplified classification if "task_complexity" in context: if context["task_complexity"] > 0.7: return "complex_task" else: return "simple_task" return "general_session" async def _update_persistent_context(self, current_persistent: Dict[str, Any], session_context: Dict[str, Any]) -> Dict[str, Any]: """Update persistent context with session information.""" # Simplified persistence logic updated_persistent = current_persistent.copy() updated_persistent["last_session"] = session_context return updated_persistent async def _identify_cross_session_patterns(self, user_id: str, persistent: Dict[str, Any], session: Dict[str, Any]) -> List[Dict[str, Any]]: """Identify patterns across sessions.""" # Simplified pattern identification return [ { "pattern_type": "session_continuity", "strength": 0.7, "context": "persistent_preferences" } ] async def _generate_continuity_recommendations(self, user_id: str, persistent: Dict[str, Any], patterns: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Generate recommendations for maintaining continuity.""" # Simplified recommendations return [ { "recommendation": "maintain_preferred_style", "priority": "high", "context": "user_communication_preferences" } ] def _calculate_continuity_strength(self, patterns: List[Dict[str, Any]]) -> float: """Calculate strength of continuity patterns.""" if not patterns: return 0.0 return np.mean([pattern.get("strength", 0.5) for pattern in patterns]) # Learning methods async def _incremental_learning(self, interaction: UserInteraction) -> Dict[str, Any]: """Incremental learning from interaction.""" return {"learning_type": "incremental", "progress": 0.1} async def _batch_learning(self, interactions: List[UserInteraction]) -> Dict[str, Any]: """Batch learning from multiple interactions.""" return {"learning_type": "batch", "progress": 0.3} async def _reinforcement_learning(self, interaction: UserInteraction) -> Dict[str, Any]: """Reinforcement learning from interaction outcome.""" return {"learning_type": "reinforcement", "reward": interaction.success} async def _association_learning(self, interaction: UserInteraction) -> Dict[str, Any]: """Association learning between context and outcomes.""" return {"learning_type": "association", "connections": 2} if __name__ == "__main__": print("Contextual Personalization & User Profiling System Initialized") print("=" * 70) engine = ContextualPersonalizationEngine() print("Ready for advanced user profiling and personalization!")