Context Pipeline

The Context Pipeline is PriveTag’s system for understanding user needs and providing relevant recommendations.

The 8 Golden Points

We capture 8 key data points that drive recommendation quality:

1. Travel Type

Family, couple, solo, business, friends

2. Nationality

Cultural preferences and language

3. Interests

Activities user enjoys

4. Budget

Spending preferences

5. Age Group

Activity difficulty matching

6. Location

Current or target area

7. Weather

Real-time conditions

8. Time

Time of day relevance

Pipeline Architecture

Data Flow Example

Input: API Request

{
  "user_profile": {
    "travel_type": "family",
    "nationality": "KR",
    "interests": ["theme-park", "zoo"],
    "budget": "mid"
  },
  "location": {
    "city": "Bangkok",
    "lat": 13.7563,
    "lon": 100.5018
  },
  "limit": 5
}

Step 1: Enrichment

The pipeline enriches the request with external data:

{
  "weather": {
    "condition": "sunny",
    "temperature": 32,
    "humidity": 65,
    "is_outdoor_suitable": true
  },
  "time_context": {
    "local_time": "14:30",
    "day_period": "afternoon",
    "is_weekend": true
  }
}

Step 2: Ground Truth Lookup

Query historical data for similar profiles:

-- What did Korean families actually visit in Bangkok?
SELECT activity_id, COUNT(*) as visits
FROM ground_truth
WHERE travel_type = 'family'
  AND nationality = 'KR'
  AND city = 'Bangkok'
  AND is_verified = TRUE
GROUP BY activity_id
ORDER BY visits DESC

Step 3: Scoring

Each activity is scored on multiple factors:

Factor	Weight	Safari World	Dream World	Night Market
Profile Match	30%	28/30	25/30	15/30
Ground Truth	25%	24/25	20/25	10/25
Weather Match	20%	18/20	18/20	12/20
Time Match	15%	14/15	14/15	8/15
Budget Match	10%	9/10	9/10	10/10
Total	100%	93	86	55

Step 4: Output

{
  "recommendations": [
    {
      "id": "act_safari",
      "title": "Safari World Bangkok",
      "relevance_score": 93,
      "recommendation_reason": "Top choice for Korean families - verified visits"
    },
    {
      "id": "act_dream",
      "title": "Dream World",
      "relevance_score": 86,
      "recommendation_reason": "Family-friendly theme park for afternoon"
    }
  ],
  "context": {
    "log_id": "ctx_abc123",
    "user_profile_summary": "Korean family, theme-park & zoo interests, mid budget",
    "environment_summary": "Bangkok, sunny 32°C, Saturday afternoon"
  }
}

Scoring Factors Explained

1. Profile Match (30%)

How well does the activity match user preferences?

User Attribute	Matching Logic
`travel_type`	Family → kid-friendly activities
`interests`	Direct category match
`budget`	Price range alignment
`age_group`	Activity difficulty level

2. Ground Truth (25%)

Real visit data from similar users:

Ground Truth Scoring: Activities that similar profiles actually visited (and verified via NFC/QR) get higher scores than those only booked.

Score = (verified_visits / total_recommendations) * 25

3. Weather Match (20%)

Real-time weather appropriateness:

Condition	Indoor Activities	Outdoor Activities
Sunny, < 35°C	15/20	20/20
Sunny, > 35°C	18/20	10/20
Rainy	20/20	5/20
Overcast	17/20	18/20

4. Time Match (15%)

Activity timing relevance:

Time Period	Best Activities
Morning (6-11)	Tours, outdoor, breakfast spots
Afternoon (11-17)	Theme parks, museums, malls
Evening (17-21)	Dinner, shows, sunset activities
Night (21+)	Nightlife, late dining

5. Budget Match (10%)

Price alignment with stated budget:

budget: "budget"  → Activities < 500 THB score highest
budget: "mid"     → Activities 500-2000 THB score highest
budget: "luxury"  → Activities > 2000 THB score highest

Context Logging

Every recommendation request creates a context log:

{
  "log_id": "ctx_abc123",
  "created_at": "2025-12-10T14:30:00Z",
  "input": {
    "user_profile": { /* ... */ },
    "location": { /* ... */ }
  },
  "enriched": {
    "weather": { /* ... */ },
    "time_context": { /* ... */ }
  },
  "ground_truth_matches": 45,
  "recommended_activities": ["act_safari", "act_dream", "act_sea"],
  "scores": {
    "act_safari": 93,
    "act_dream": 86,
    "act_sea": 78
  }
}

Why Context Logging Matters

When a user later books and visits an activity: This feedback loop is what makes PriveTag’s recommendations improve over time.

Customization Options

Filters

Override automatic scoring with explicit filters:

{
  "filters": {
    "is_indoor": true,      // Force indoor only
    "max_price": 2000,      // Price ceiling
    "categories": ["spa"],  // Specific categories
    "difficulty": "easy"    // Activity intensity
  }
}

Boosting

Boost specific factors for the request:

{
  "scoring_weights": {
    "ground_truth": 0.4,    // Increase GT weight from 25% to 40%
    "weather": 0.1          // Decrease weather weight from 20% to 10%
  }
}

Performance

Metric	Value
Average Processing Time	< 200ms
Weather API Cache TTL	15 minutes
Ground Truth Query Cache	1 hour
Max Concurrent Requests	1000/sec

Best Practices

Always provide lat/lon when available

Location coordinates enable weather-aware recommendations. Without them, we default to city-level weather which may be less accurate.

Include all known user attributes

More context = better recommendations. Even if optional, providing nationality, age_group, and budget significantly improves relevance.

Use context_log_id for bookings

Pass the log_id when booking to complete the feedback loop. This is how Ground Truth data improves over time.

Don't over-filter

Let the scoring algorithm work. Excessive filters can eliminate good options that would score highly.

B2A Platform

Context Pipeline

Context Pipeline

The 8 Golden Points

1. Travel Type

2. Nationality

3. Interests

4. Budget

5. Age Group

6. Location

7. Weather

8. Time

Pipeline Architecture

Data Flow Example

Input: API Request

Step 1: Enrichment

Step 2: Ground Truth Lookup

Step 3: Scoring

Step 4: Output

Scoring Factors Explained

1. Profile Match (30%)

2. Ground Truth (25%)

3. Weather Match (20%)

4. Time Match (15%)

5. Budget Match (10%)

Context Logging

Why Context Logging Matters

Customization Options

Filters

Boosting

Performance

Best Practices

Next Steps

Ground Truth

API Reference

​Context Pipeline

​The 8 Golden Points

1. Travel Type

2. Nationality

3. Interests

4. Budget

5. Age Group

6. Location

7. Weather

8. Time

​Pipeline Architecture

​Data Flow Example

​Input: API Request

​Step 1: Enrichment

​Step 2: Ground Truth Lookup

​Step 3: Scoring

​Step 4: Output

​Scoring Factors Explained

​1. Profile Match (30%)

​2. Ground Truth (25%)

​3. Weather Match (20%)

​4. Time Match (15%)

​5. Budget Match (10%)

​Context Logging

​Why Context Logging Matters

​Customization Options

​Filters

​Boosting

​Performance

​Best Practices

​Next Steps

Ground Truth

API Reference

Context Pipeline

The 8 Golden Points

Pipeline Architecture

Data Flow Example

Input: API Request

Step 1: Enrichment

Step 2: Ground Truth Lookup

Step 3: Scoring

Step 4: Output

Scoring Factors Explained

1. Profile Match (30%)

2. Ground Truth (25%)

3. Weather Match (20%)

4. Time Match (15%)

5. Budget Match (10%)

Context Logging

Why Context Logging Matters

Customization Options

Filters

Boosting

Performance

Best Practices

Next Steps