Design short-video retrieval with sparse text

Q: Design short-video retrieval with sparse text

This question evaluates expertise in ML system design, large-scale information retrieval and recommendation engineering, with emphasis on multi-modal video representation, sparse-text handling, indexing/ANN choices, low-latency online serving, and bias mitigation such as popularity bias.

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Question

You are designing the candidate-generation (retrieval) and recommendation system for a short-video app.

Constraints and setting:

Users can search with a text query (e.g., “funny cat fails”), and the system should retrieve relevant short videos .
Only ~20% of videos have reliable text metadata (title/description/hashtags). The rest may have only visual/audio signals.
You must support low-latency online retrieval at large scale.

Tasks:

Propose an end-to-end architecture for query-to-video retrieval and how it fits into a full recommender stack (retrieval → ranking → re-ranking).
Explain how you would represent videos (multi-modal features) and queries, and how you would handle the 80% of videos without text.
Describe offline training, online serving, indexing/ANN choices, and how you would evaluate retrieval quality.
Discuss how you would mitigate popularity bias in retrieval/recommendation while keeping relevance and engagement strong.

Design short-video retrieval with sparse text

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