Design a Local Recommendation System
Company: ByteDance
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
How would you build a local recommendation system? Begin by clarifying what local means and what is being recommended.
### Constraints and Clarifying Questions
The report supplies no item type, objective, data, or definition of local. For a concrete worked design, interpret local as geographically nearby content or places and state that assumption. If local instead means on-device execution, the serving and privacy constraints change substantially. Define relevance, location freshness, eligibility, and the user action the system should support before selecting a model.
### What a Strong Answer Covers
A clear problem definition, geographic candidate generation, ranking signals and labels, cold start, evaluation, and treatment of location uncertainty and privacy. Distinguish proposed features from known available data.
### Follow-up Questions
- How would the system behave when location permission is unavailable or stale?
- What would change if the intended meaning were an on-device recommender?
Overview: Define and design a local recommender while making the meaning of local explicit. Cover geographic candidates, ranking, sparse-data behavior, location uncertainty, and the changes required for on-device execution.