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Explain ranking cold-start strategies

GoogleMachine Learning Engineer · Technical Screen
MediumMachine Learning EngineerTechnical ScreenMachine Learning

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This question tests your ability to design strategies for surfacing new items and serving new users in a large-scale recommendation system while maintaining relevance and diversity.

The Problem

You are working on the recommendation engine for a video streaming platform that ingests millions of new videos every day. You face two major cold-start challenges:

  1. Item Cold-Start: New videos have zero interaction history (clicks, watch time), making collaborative filtering ineffective.
  2. User Cold-Start: New or logged-out users have no historical preference profile.

Explain how you would design a system to address these cold-start scenarios within a two-stage recommendation architecture (Candidate Generation and Ranking). Specifically, address the following:

  1. Feature Engineering: What features (meta-data, content-based, or contextual) would you prioritize to bootstrap these entities?
  2. Model Strategy: How would you adjust your candidate generation retrieval and your final ranking model to account for the lack of interaction data?
  3. Exploration vs. Exploitation: How would you balance the need to collect data on new items (exploration) without significantly degrading the user experience?

Include a discussion on how you would represent these entities (e.g., using content-based embeddings) and how you would evaluate the success of your cold-start strategy.

Hints

Hint 1

Consider using multi-modal embeddings, such as visual or audio features, to map new items into the same latent space as existing, popular items.

Hint 2

For new users, think about how contextual features like geolocation, device type, or session-level behavior can serve as proxies for long-term user preferences.

Hint 3

Think about implementing a multi-armed bandit approach or epsilon-greedy strategy to systematically interleave new items into the recommendation stream.

Follow-up questions

  • How would you handle the 'popularity bias' that occurs when your model consistently favors high-interaction items over new, potentially high-quality content?
  • If you have a limited compute budget for inference, how would you optimize the retrieval of items for a user with no history?
  • How would you detect when a new item has successfully transitioned out of the cold-start phase?