​

Personalizer

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What Was Personalizer?

• Rank actions: Determine the best action for a given context
• Learn from feedback: Improve decisions based on rewards
• Personalize at scale: Optimize for all users collectively
• Adapt in real-time: Update models continuously

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How It Worked

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Rank API

from azure.cognitiveservices.personalizer import PersonalizerClient
from azure.cognitiveservices.personalizer.models import RankRequest

client = PersonalizerClient(endpoint, credentials)

# Define actions (content options)
actions = [
    {"id": "article1", "features": [{"topic": "tech", "length": "short"}]},
    {"id": "article2", "features": [{"topic": "sports", "length": "long"}]},
    {"id": "article3", "features": [{"topic": "news", "length": "medium"}]}
]

# Define context (user and situation)
context = [
    {"user": {"location": "US", "device": "mobile"}},
    {"time": {"dayOfWeek": "Monday", "hour": 14}}
]

# Get ranking
rank_request = RankRequest(
    actions=actions,
    context_features=context,
    event_id="unique-event-id"
)

response = client.rank(rank_request)

# Use top-ranked action
top_action = response.ranking[0]
print(f"Show content: {top_action.id}")
print(f"Probability: {top_action.probability}")

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Reward API

# User clicked on the article (positive outcome)
client.events.reward(
    event_id="unique-event-id",
    value=1.0  # Reward score: 0 (bad) to 1 (good)
)

# User ignored the article (negative outcome)
client.events.reward(
    event_id="unique-event-id",
    value=0.0
)

# Partial reward for incomplete action
client.events.reward(
    event_id="unique-event-id",
    value=0.5
)

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Key Concepts

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Actions

• ID: Unique identifier for the action
• Features: Characteristics describing the action
• Example: Articles, products, ads, recommendations

{
  "id": "product-123",
  "features": [
    {"category": "electronics", "price": "high", "brand": "premium"}
  ]
}

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Context

• User features: Demographics, preferences, history
• Environment features: Time, device, location
• Session features: Page type, previous actions

[
  {"user": {"age": "25-34", "interests": ["tech", "gaming"]}},
  {"device": {"type": "mobile", "os": "iOS"}},
  {"time": {"hour": 20, "dayOfWeek": "Friday"}}
]

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Rewards

• Range: 0 (worst) to 1 (best)
• Timing: Real-time or delayed
• Meaning: Defined by your business objectives

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Learning Modes

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Apprentice Mode

• Learn from your existing logic
• No impact on user experience
• Validate features and configuration
• Returns your baseline action
• Build confidence before going live

# In Apprentice mode, baseline action is returned
rank_request = RankRequest(
    actions=actions,
    context_features=context,
    baseline_action="default-content"  # Your current logic
)

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Online Mode

• Returns best predicted action
• Explores alternative actions
• Learns from all feedback
• Continuously improves
• Optimizes for reward maximization

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Use Cases

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Content Personalization

• News article recommendations
• Video suggestions
• Product recommendations
• Ad placement optimization
• Email content selection

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User Experience

• Homepage layout personalization
• Feature highlighting
• Navigation customization
• Search result ranking
• Notification timing

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E-commerce

• Product recommendations
• Promotional offers
• Pricing strategies
• Bundle suggestions
• Upsell opportunities

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Communication

• Push notification timing
• Email subject lines
• Message content selection
• Communication channel choice

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Features and Configuration

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Feature Engineering

• Categorical: Device type, location, category
• Numerical: Price, rating, popularity
• Boolean: Is premium, has discount, in stock
• Text: Tags, keywords (as categorical)

• Use 5-50 features per action/context
• Include diverse feature types
• Avoid highly correlated features
• Update features as they change
• Test feature importance

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Model Configuration

• Update frequency: How often model trains
• Exploration percentage: Random action probability
• Reward wait time: Delay for reward signal
• Default reward: Reward when none provided

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Evaluation

• Offline evaluation: Compare strategies on historical data
• Online evaluation: A/B test in production
• Counterfactual evaluation: Estimate alternative policies

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Migration Alternatives

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Azure AI Solutions

• Azure OpenAI: For LLM-based recommendations
• Azure Machine Learning: Custom recommendation models
• Azure Applied AI: Pre-built AI solutions

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Recommendation Systems

• Collaborative filtering: User-item matrix factorization
• Content-based filtering: Feature-based matching
• Hybrid approaches: Combine multiple techniques

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Third-Party Services

• Amazon Personalize
• Google Recommendations AI
• Algolia Recommend
• Custom ML solutions

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Best Practices (Historical)

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Data Requirements

• Minimum 1,000 events per day for learning
• Consistent feature schema
• Quality reward signals
• Sufficient action variety (50 max recommended)

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Feature Design

• Use relevant, non-redundant features
• Include both action and context features
• Update features when they change
• Test feature importance with evaluations

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Reward Function

• Align with business objectives
• Provide timely feedback
• Use consistent scale (0-1)
• Consider delayed rewards for complex goals

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Testing

• Start in Apprentice mode
• Run offline evaluations
• A/B test before full deployment
• Monitor metrics continuously

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SDK Support (Historical)

pip install azure-cognitiveservices-personalizer

dotnet add package Microsoft.Azure.CognitiveServices.Personalizer

npm install @azure/cognitiveservices-personalizer

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Migration Guidance

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Resources

• Retirement Announcement
• Historical Documentation
• Code Samples
• Migration Guide

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Support

• Review retirement timeline and deadlines
• Plan migration strategy
• Export critical data
• Implement alternative solutions
• Contact Azure support for migration assistance