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Diversity and Freshness in Feed - Avoiding Filter Bubbles

SYSTEM-DESIGNhardInterview freq: high
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🤖
Diversity and Freshness in Feed - Avoiding Filter Bubbles
Design a recommendation system that balances diversity and freshness to avoid filter bubbles.
system-designhardFreq: highRecommendations
Prerequisites:Feed Ranking SystemResponsible AI
🎯
Problem Framing
Business Objective

Maximize user engagement by providing a diverse and fresh content feed.

ML Objective

Optimize for a balance between relevance, diversity, and freshness in content recommendations.

Constraints
  • P99 latency < 100ms for feed generation
  • Real-time user interaction data availability
  • Cold start problem for new users and items
  • Regulatory compliance for content diversity
Clarifying Questions
  • What is the current user engagement metric we are optimizing?
  • How do we define and measure content diversity?
  • What is the acceptable trade-off between relevance and diversity?
  • How frequently does the content need to be refreshed?
  • What are the data privacy considerations for user data?
Out of Scope
✗ Content moderation policies
✗ User interface design
✗ Backend infrastructure scaling
📊
Offline Metrics
NDCG@10 because it weights top results more, ensuring relevance
Diversity score to measure the variety of content types in the feed
📈
Online Metrics
CTR to measure immediate user engagement
Session duration to assess long-term engagement
⚖️
Trade-offs

When optimizing for diversity, CTR might initially drop as users are exposed to less familiar content.

💼
Business Metric

Overall user engagement measured by average session duration and CTR.

🗄️
Data Sources
  • User interaction logs (clicks, likes, shares) - real-time
  • Content metadata (category, tags) - updated daily
  • User profile data (preferences, history) - updated in real-time
🏷️
Data Properties
Labeling Strategy

Implicit feedback from user interactions such as clicks and likes.

Data Volume

Approximately 100 million interactions per day, 10 TB/month

Freshness

Features should be updated in real-time to reflect the latest user interactions.

⚠️
Data Challenges
  • Sparse labels for new content
  • Position bias in user interactions
  • Survivorship bias in content popularity
  • Cold start for new users and items
🔧
Key Features
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feat
⚡
Feature Engineering Notes
Real-time vs Batch

User embeddings and interaction features need real-time computation, while content embeddings can be pre-computed.

Feature Store

Required

🐍
Feature Snippet
Python
def compute_user_embedding(user_id, interaction_logs):
    # Example function to compute user embedding
    interactions = get_recent_interactions(user_id, interaction_logs)
    return aggregate_embeddings(interactions)
🤖
Model Selection
Baseline

Popularity-based model

Proposed Model

Two-tower neural network model

The two-tower model allows for efficient computation of user-item interactions and supports pre-computed embeddings, reducing online latency.
✗
Alternatives Rejected
Collaborative filtering - fails to capture real-time user preferences and diversity
XGBoost - lacks the ability to efficiently handle high-dimensional embeddings and real-time updates
🏋️
Training Strategy

Hybrid approach with offline training for embeddings and online learning for real-time updates.

🚀
Serving Infrastructure
LatencyP99 < 100ms end-to-end
Throughput10,000 QPS
ArchitectureTwo-stage (retrieval + ranking) to efficiently handle large candidate sets and ensure diverse recommendations.
CachingCache popular content embeddings with a TTL of 1 hour, invalidate on content update.
FallbackUse a rule-based system to recommend trending content if the model is unavailable.
📡
Data Drift Signals
Shift in user interaction distribution
Change in content category distribution
Decrease in diversity score
📉
Model Drift Signals
Drop in NDCG@10
Increase in prediction error variance
Decrease in CTR
🔔
Alerting & Retraining
Retraining Trigger

Retrain when NDCG@10 drops by more than 5% over a week.

Alerting Strategy

Page on-call engineer if CTR drops by 10% in a day, log all prediction errors, auto-recover by switching to fallback.

🎯
Interview Angles
  • How do you balance relevance and diversity in recommendations?
  • What are the challenges of real-time feature updates?
  • How do you handle the cold start problem in this system?
⚠️
Common Mistakes
Ignoring the trade-off between diversity and relevance
Overlooking the importance of real-time data freshness
Failing to define clear metrics for diversity
Neglecting the cold start problem for new users and items
📌
Key Decisions
  • Choosing a two-tower model for efficient embedding handling
  • Implementing a hybrid training strategy for real-time updates
  • Designing a two-stage serving architecture for scalability

Practice

(1/5)
1. In a real-time fraud detection system, which component is responsible for scoring transactions as they occur?
easy
A. Real-time scoring engine
B. Offline model training
C. Data warehouse
D. Batch processing engine

Solution

  1. Step 1: Identify the component for real-time scoring.

    The real-time scoring engine is designed to evaluate transactions as they occur.

  2. Step 2: Eliminate wrong options.

    Batch processing engine (A) is for batch jobs, Data warehouse (C) is for storage, and Offline model training (D) is for model development.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Real-time scoring needs a real-time engine. [OK]
Hint: Real-time scoring needs a real-time engine. [OK]
Common Mistakes:
2. For a fraud detection system, which metric is most appropriate to optimize for minimizing false positives?
easy
A. Recall
B. Accuracy
C. Precision
D. F1 Score

Solution

  1. Step 1: Understand the impact of false positives.

    Minimizing false positives means correctly identifying non-fraudulent transactions.

  2. Step 2: Choose the metric that reflects this goal.

    Precision is the metric that focuses on reducing false positives.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Precision reduces false positives. [OK]
Hint: Precision reduces false positives. [OK]
Common Mistakes:
3. For a news feed system aiming to improve user engagement, which metric should be prioritized?
easy
A. Click-Through Rate (CTR)
B. Page Load Time
C. Server Uptime
D. Data Storage Cost

Solution

  1. Step 1: Align metrics with business goals

    CTR directly measures user engagement by tracking how often users click on recommended content.

  2. Step 2: Eliminate wrong options

    B: Page Load Time affects user experience but not engagement directly. C: Server Uptime is about reliability, not engagement. D: Data Storage Cost is a financial metric, not related to user engagement.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    CTR measures user engagement directly. [OK]
Hint: CTR measures user engagement directly. [OK]
Common Mistakes:
4. In the context of intent classification, which of the following techniques is most suitable for handling imbalanced datasets?
easy
A. Apply SMOTE (Synthetic Minority Over-sampling Technique)
B. Use a simple logistic regression model
C. Use a linear SVM without any modifications
D. Ignore the imbalance and focus on feature engineering

Solution

  1. Step 1: Identify the challenge of imbalanced datasets.

    Imbalanced datasets can lead to biased models that favor the majority class.

  2. Step 2: Evaluate each option.

    Use a simple logistic regression model: Logistic regression without addressing imbalance will likely be biased. Use a linear SVM without any modifications: Linear SVM also suffers from imbalance without adjustments. Ignore the imbalance and focus on feature engineering: Ignoring imbalance is not a solution. Apply SMOTE (Synthetic Minority Over-sampling Technique): SMOTE is specifically designed to handle imbalance by generating synthetic samples.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    SMOTE for imbalance handling. [OK]
Hint: SMOTE is key for imbalanced datasets. [OK]
Common Mistakes:
5. As the data volume increases tenfold in an ML monitoring platform, which component is most likely to become a bottleneck first?
hard
A. Retraining Scheduler
B. Alerting System
C. Drift Detection Module
D. Model Evaluation Component

Solution

  1. Step 1: Analyze component scalability

    The Drift Detection Module processes incoming data to identify changes, making it sensitive to data volume increases.

  2. Step 2: Consider computational load

    Drift detection often involves statistical tests or complex calculations, which can become a bottleneck with large data volumes.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Data increase = Drift bottleneck [OK]
Hint: Data increase = Drift bottleneck [OK]
Common Mistakes:
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