Design homepage store recommendations

Q: Design homepage store recommendations

This question evaluates competency in end-to-end recommender system design and production ML engineering, including retrieval, filtering, ranking, feature storage, geographic caching, latency optimization, and model evolution.

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Question

Design a DoorDash-like homepage recommendation system for local stores/restaurants.

Request context

The online request contains very little information: user_id and the user's current latitude/longitude.
The system must return a ranked list of stores for the app home feed.

Hard constraints

A recommended store must be within the user's delivery/service area .
A recommended store must be currently open .
The system is latency-sensitive and must support very low end-to-end serving time.

Tasks

Propose the end-to-end architecture for retrieval, filtering, ranking, and serving.
Explain how you would design candidate retrieval given sparse online inputs.
Describe how to use geographic caching (for example, Geohash or a grid-cell system) so that offline-computed local hot-store lists can be reused online through a KV store such as DynamoDB.
Suppose each retrieval source has an extremely tight timeout budget (for example, about 15 ms ). How would you optimize parallel fan-out, caching, fallbacks, and concurrency ?
Design the ranking layer and feature platform :
- How would you store different feature types such as embeddings, numerical features, and categorical features?
- What keys would you use for user, store, and user-store features?
- How would you support hourly offline refreshes while preserving efficient online reads for inference?
Explain how the infrastructure should support model evolution , such as moving from model V1.1 to V2.0 with additional features.
Discuss the trade-offs between real-time features and batch/offline features under strict latency requirements, including failure modes such as timeouts, missing values, feature freshness issues, and training-serving skew.
What online and offline metrics would you monitor to judge system quality, latency, and marketplace health?

Design homepage store recommendations

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