136 lines
8.2 KiB
Markdown
136 lines
8.2 KiB
Markdown
---
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title: auto-learn-preference-signals
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type: note
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permalink: voyage/research/auto-learn-preference-signals
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---
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# Research: Auto-Learn User Preference Signals
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## Purpose
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Map all existing user data that could be aggregated into an automatic preference profile, without requiring manual input.
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## Signal Inventory
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### 1. Location.category (FK → Category)
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- **Model**: `adventures/models.py:Category` — per-user custom categories (name, display_name, icon)
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- **Signal**: Top categories by count → dominant interest type (e.g. "hiking", "dining", "cultural")
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- **Query**: `Location.objects.filter(user=user).values('category__name').annotate(cnt=Count('id')).order_by('-cnt')`
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- **Strength**: HIGH — user-created categories are deliberate choices
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### 2. Location.tags (ArrayField)
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- **Model**: `adventures/models.py:Location.tags` — `ArrayField(CharField(max_length=100))`
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- **Signal**: Most frequent tags across all user locations → interest keywords
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- **Query**: `Location.objects.filter(user=user).values_list('tags', flat=True).distinct()` (used in `tags_view.py`)
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- **Strength**: MEDIUM-HIGH — tags are free-text user input
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### 3. Location.rating (FloatField)
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- **Model**: `adventures/models.py:Location.rating`
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- **Signal**: Average rating + high-rated locations → positive sentiment for place types; filtering for visited + high-rated → strong preferences
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- **Query**: `Location.objects.filter(user=user).aggregate(avg_rating=Avg('rating'))` or breakdown by category
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- **Strength**: HIGH for positive signals (≥4.0); weak if rarely filled in
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### 4. Location.description / Visit.notes (TextField)
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- **Model**: `adventures/models.py:Location.description`, `Visit.notes`
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- **Signal**: Free-text content for NLP keyword extraction (budget, adventure, luxury, cuisine words)
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- **Query**: `Location.objects.filter(user=user).values_list('description', flat=True)`
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- **Strength**: LOW (requires NLP to extract structured signals; many fields blank)
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### 5. Lodging.type (LODGING_TYPES enum)
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- **Model**: `adventures/models.py:Lodging.type` — choices: hotel, hostel, resort, bnb, campground, cabin, apartment, house, villa, motel
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- **Signal**: Most frequently used lodging type → travel style indicator (e.g. "hostel" → budget; "resort/villa" → luxury; "campground/cabin" → outdoor)
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- **Query**: `Lodging.objects.filter(user=user).values('type').annotate(cnt=Count('id')).order_by('-cnt')`
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- **Strength**: HIGH — directly maps to trip_style field
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### 6. Lodging.rating (FloatField)
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- **Signal**: Combined with lodging type, identifies preferred accommodation standards
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- **Strength**: MEDIUM
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### 7. Transportation.type (TRANSPORTATION_TYPES enum)
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- **Model**: `adventures/models.py:Transportation.type` — choices: car, plane, train, bus, boat, bike, walking
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- **Signal**: Primary transport mode → mobility preference (e.g. mostly walking/bike → slow travel; lots of planes → frequent flyer)
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- **Query**: `Transportation.objects.filter(user=user).values('type').annotate(cnt=Count('id')).order_by('-cnt')`
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- **Strength**: MEDIUM
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### 8. Activity.sport_type (SPORT_TYPE_CHOICES)
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- **Model**: `adventures/models.py:Activity.sport_type` — 60+ choices mapped to 10 SPORT_CATEGORIES in `utils/sports_types.py`
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- **Signal**: Activity categories user is active in → physical/adventure interests
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- **Categories**: running, walking_hiking, cycling, water_sports, winter_sports, fitness_gym, racket_sports, climbing_adventure, team_sports
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- **Query**: Already aggregated in `stats_view.py:_get_activity_stats_by_category()` — uses `Activity.objects.filter(user=user).values('sport_type').annotate(count=Count('id'))`
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- **Strength**: HIGH — objective behavioral data from Strava/Wanderer imports
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### 9. VisitedRegion / VisitedCity (worldtravel)
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- **Model**: `worldtravel/models.py` — `VisitedRegion(user, region)` and `VisitedCity(user, city)` with country/subregion
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- **Signal**: Countries/regions visited → geographic preferences (beach vs. mountain vs. city; EU vs. Asia etc.)
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- **Query**: `VisitedRegion.objects.filter(user=user).select_related('region__country')` → country distribution
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- **Strength**: MEDIUM-HIGH — "where has this user historically traveled?" informs destination type
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### 10. Collection metadata
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- **Model**: `adventures/models.py:Collection` — name, description, start/end dates
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- **Signal**: Collection names/descriptions may contain destination/theme hints; trip duration (end_date − start_date) → travel pace; trip frequency (count, spacing) → travel cadence
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- **Query**: `Collection.objects.filter(user=user).values('name', 'description', 'start_date', 'end_date')`
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- **Strength**: LOW-MEDIUM (descriptions often blank; names are free-text)
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### 11. Location.price / Lodging.price (MoneyField)
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- **Signal**: Average spend across locations/lodging → budget tier
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- **Query**: `Location.objects.filter(user=user).aggregate(avg_price=Avg('price'))` (requires djmoney amount field)
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- **Strength**: MEDIUM — but many records may have no price set
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### 12. Location geographic clustering (lat/lon)
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- **Signal**: Country/region distribution of visited locations → geographic affinity
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- **Already tracked**: `Location.country`, `Location.region`, `Location.city` (FK, auto-geocoded)
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- **Query**: `Location.objects.filter(user=user).values('country__name').annotate(cnt=Count('id')).order_by('-cnt')`
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- **Strength**: HIGH
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### 13. UserAchievement types
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- **Model**: `achievements/models.py:UserAchievement` — types: `adventure_count`, `country_count`
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- **Signal**: Milestone count → engagement level (casual vs. power user); high `country_count` → variety-seeker
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- **Strength**: LOW-MEDIUM (only 2 types currently)
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### 14. ChatMessage content (user role)
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- **Model**: `chat/models.py:ChatMessage` — `role`, `content`
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- **Signal**: User messages in travel conversations → intent signals ("I love hiking", "looking for cheap food", "family-friendly")
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- **Query**: `ChatMessage.objects.filter(conversation__user=user, role='user').values_list('content', flat=True)`
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- **Strength**: MEDIUM — requires NLP; could be rich but noisy
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## Aggregation Patterns Already in Codebase
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| Pattern | Location | Reusability |
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|---|---|---|
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| Activity stats by category | `stats_view.py:_get_activity_stats_by_category()` | Direct reuse |
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| All-tags union | `tags_view.py:ActivityTypesView.types()` | Direct reuse |
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| VisitedRegion/City counts | `stats_view.py:counts()` | Direct reuse |
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| Multi-user preference merge | `llm_client.py:get_aggregated_preferences()` | Partial reuse |
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| Category-filtered location count | `serializers.py:location_count` | Pattern reference |
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| Location queryset scoping | `location_view.py:get_queryset()` | Standard pattern |
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## Proposed Auto-Profile Fields from Signals
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| Target Field | Primary Signals | Secondary Signals |
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|---|---|---|
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| `cuisines` | Location.tags (cuisine words), Location.category (dining) | Location.description NLP |
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| `interests` | Activity.sport_type categories, Location.category top-N | Location.tags frequency, VisitedRegion types |
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| `trip_style` | Lodging.type top (luxury/budget/outdoor), Transportation.type, Activity sport categories | Location.rating Avg, price signals |
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| `notes` | (not auto-derived — keep manual only) | — |
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## Where to Implement
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**New function target**: `integrations/views/recommendation_profile_view.py` or a new `integrations/utils/auto_profile.py`
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**Suggested function signature**:
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```python
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def build_auto_preference_profile(user) -> dict:
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"""
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Returns {cuisines, interests, trip_style} inferred from user's travel history.
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Fields are non-destructive suggestions, not overrides of manual input.
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"""
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```
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**New API endpoint target**: `POST /api/integrations/recommendation-preferences/auto-learn/`
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**ViewSet action**: `@action(detail=False, methods=['post'], url_path='auto-learn')` on `UserRecommendationPreferenceProfileViewSet`
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## Integration Point
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`get_system_prompt()` in `chat/llm_client.py` already consumes `UserRecommendationPreferenceProfile` — auto-learned values
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flow directly into AI context with zero additional changes needed there.
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See: [knowledge.md — User Recommendation Preference Profile](../knowledge.md#user-recommendation-preference-profile)
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See: [plans/ai-travel-agent-redesign.md — WS2](../plans/ai-travel-agent-redesign.md#ws2-user-preference-learning) |