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Diversity and Freshness in Feed - Avoiding Filter Bubbles

SYSTEM-DESIGNhardInterview freq: high
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🎯
Design a Personalized Feed with Diversity and Freshness
45 min · senior
senior

Your team of 4 engineers has 6 months to design a personalized feed for a social media app with 10M DAU. You have implicit feedback (clicks, dwell time) but no explicit ratings. The P99 latency SLA is 100ms. The system must balance personalization with content diversity and freshness to avoid filter bubbles.

Given Info
  • 10M DAU with 100M daily interactions
  • Implicit feedback available: clicks, dwell time
  • No explicit ratings available
  • P99 latency SLA is 100ms
  • Team of 4 engineers, 6-month timeline
📋
Guided Steps
1/5
Step 1
Problem Framing and Objective Setting
How would you frame the problem and set the objectives for this feed recommendation system?
💡 Hint
Consider both business goals (user engagement) and ML objectives (diversity, freshness).
Key Decision
Define ML objectives as maximizing user engagement while ensuring content diversity and freshness.
Interviewer Expectation
Mid: Identifies user engagement as a key objective. Senior: Also articulates the need for diversity and freshness to prevent filter bubbles, linking them to business goals.
⚠️Common Wrong Answer
Focusing solely on maximizing click-through rate without considering diversity or freshness.
Step 2
Data Pipeline and Feature Engineering
What data pipeline and feature engineering strategies would you employ?
💡 Hint
Think about how to handle implicit feedback and ensure data freshness.
Key Decision
Use real-time data processing for implicit feedback to maintain data freshness.
Interviewer Expectation
Mid: Proposes batch processing of click data. Senior: Suggests a hybrid approach with real-time processing for freshness and batch for historical trends.
⚠️Common Wrong Answer
Relying solely on batch processing, leading to stale data and delayed reactions to user behavior changes.
Step 3
Model Selection and Training
Which model architecture would you choose and why?
💡 Hint
Consider the trade-offs between model complexity and serving latency.
Key Decision
Select a two-tower model for efficient online inference while maintaining personalization.
Interviewer Expectation
Mid: Chooses a simple collaborative filtering model. Senior: Justifies a two-tower model for its balance of personalization and low latency, explaining the trade-offs.
⚠️Common Wrong Answer
Choosing a complex model like a deep neural network without considering latency constraints.
Step 4
Serving and Latency Management
How would you design the serving infrastructure to meet the latency SLA?
💡 Hint
Consider caching strategies and model inference optimizations.
Key Decision
Implement caching and model distillation to reduce inference time.
Interviewer Expectation
Mid: Proposes basic caching. Senior: Details a layered caching strategy and model distillation to ensure P99 latency under 100ms.
⚠️Common Wrong Answer
Ignoring caching, leading to high latency during peak loads.
Step 5
Evaluation and Monitoring
What metrics would you use to evaluate and monitor the system?
💡 Hint
Differentiate between offline and online metrics, and consider business impact.
Key Decision
Use NDCG@10 and diversity score offline; monitor CTR and user session length online.
Interviewer Expectation
Mid: Focuses on CTR and NDCG. Senior: Also includes diversity and freshness metrics, explaining their importance.
⚠️Common Wrong Answer
Relying solely on CTR without considering diversity or freshness metrics.
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Defines ML objectives including diversity and freshness.
  • Proposes a data pipeline that ensures data freshness.
  • Selects a model architecture that balances personalization and latency.
  • Designs a serving infrastructure that meets the latency SLA.
  • Differentiates between offline and online metrics, including diversity.
Good to Have - 30%
  • Explains the trade-offs of model complexity vs. latency.
  • Proposes a hybrid data processing approach.
  • Includes a detailed caching strategy.
  • Mentions monitoring for distribution shifts.
Bonus - 10%
  • Quantifies the impact of diversity on user engagement.
  • Suggests a method for continuous model retraining.
  • Proposes a fallback strategy for model failures.
✅
Model Answer
▼ Reveal

The ideal solution defines clear ML objectives that include diversity and freshness, proposes a hybrid data pipeline for real-time and batch processing, selects a two-tower model for efficient personalization, designs a serving infrastructure with caching to meet latency requirements, and uses a mix of offline and online metrics to evaluate the system.

Key Decisions
  • Define ML objectives to include diversity and freshness to align with business goals.
  • Implement a hybrid data pipeline to ensure data freshness and capture historical trends.
  • Select a two-tower model for its balance of personalization and low latency.
  • Design a serving infrastructure with caching and model distillation to meet latency SLA.
  • Use NDCG@10 and diversity score offline; monitor CTR and user session length online.
Anti-patterns
Focusing solely on maximizing CTR without considering diversity.
Relying only on batch processing, leading to stale data.
Choosing a complex model that cannot meet latency constraints.
Ignoring the need for monitoring distribution shifts.

Practice

(1/5)
1. 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:
2. In the context of semantic search, which component is responsible for encoding both queries and documents into a shared vector space?
easy
A. Bi-Encoder
B. Cross-Encoder
C. Dense Retriever
D. Sparse Retriever

Solution

  1. Step 1: Identify the role of each component

    Bi-Encoders encode both queries and documents into a shared vector space for efficient retrieval.

  2. Step 2: Eliminate wrong options

    Cross-Encoders evaluate query-document pairs directly, Dense Retrievers use encoded vectors, Sparse Retrievers use traditional methods.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Bi-Encoder = shared vector space [check]
Hint: Bi-Encoder = shared vector space [check]
Common Mistakes:
3. In a text embedding pipeline, what is a likely failure mode if embeddings are not updated regularly with new data?
medium
A. Decreased model relevance
B. Model overfitting
C. Faster inference times
D. Increased computational cost

Solution

  1. Step 1: Consider the impact of stale embeddings.

    Embeddings that are not updated may not capture recent trends, leading to decreased relevance.

  2. Step 2: Eliminate wrong options.

    Increased cost and overfitting are unrelated to stale embeddings, and inference times are not directly affected.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Stale embeddings reduce relevance. [OK]
Hint: Stale embeddings reduce relevance. [OK]
Common Mistakes:
4. In a delivery forecasting system, when should you prefer using a simple linear regression model over a complex deep learning model?
medium
A. When the dataset is extremely large and complex
B. When interpretability and simplicity are more important than accuracy
C. When the data has non-linear patterns
D. When the model needs to capture intricate temporal dependencies

Solution

  1. Step 1: Consider the trade-offs between model complexity and interpretability

    Simple models are easier to interpret and explain.

  2. Step 2: Eliminate wrong options

    Complex models are needed for large, complex datasets (A), non-linear patterns (C), and intricate dependencies (D).

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Choose simplicity for interpretability. [OK]
Hint: Choose simplicity for interpretability. [OK]
Common Mistakes:
5. In a pairwise learning to rank system, what is a common failure mode if the model is not performing well?
medium
A. The model is overfitting due to too many features
B. The model is not capturing the relative order of item pairs
C. The model is not considering the entire list of items
D. The model is using an inappropriate loss function for regression

Solution

  1. Step 1: Identify the failure mode specific to pairwise ranking.

    Pairwise models can fail if they don't capture the relative order of item pairs.

  2. Step 2: Eliminate wrong options.

    Overfitting and inappropriate loss functions are generic issues, and pairwise doesn't consider the entire list, making C incorrect.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Pairwise = focus on item pairs [OK]
Hint: Pairwise = focus on item pairs [OK]
Common Mistakes:
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