Explain ranking cold-start strategies
Company: Google
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
You are interviewing for a machine learning engineer role focused on search, recommendations, or ranking. Discuss the following in the context of a large-scale video platform:
- How would you handle **cold start** for new users and new items?
- What are **content-based embeddings**, how are they constructed, and why are they useful in recommendation or ranking?
- How would you use different feature groups such as user features, item features, context features, freshness signals, and interaction features?
- How would these features be used differently in candidate generation versus final ranking?
- What offline and online metrics would you use to evaluate whether the approach is working?
Answer at the level expected for an applied ranking or recommendation ML interview.
Quick Answer: This question evaluates an engineer's competency in handling cold-start for users and items, constructing and applying content-based embeddings, organizing feature groups (user, item, context, freshness, interaction), and distinguishing candidate generation from final ranking in large-scale video search and recommendation systems.