Start by clarifying requirements and scale, then propose a dual-encoder architecture that maps text and video into a shared embedding space, followed by a two-stage retrieval pipeline (ANN search + re-ranking). Discuss infrastructure choices for billion-scale indexing, latency, and how to evaluate and iterate with A/B tests.
Pro tip: Emphasize that the embedding space must be trained with contrastive losses on large-scale video-text pairs, and that hard negative mining and modality gap reduction are critical for cross-modal retrieval quality. Also, mention that you'd start with a simpler baseline (e.g., CLIP-style) and iterate based on offline metrics before scaling.
Ask about query types (text, video, image), latency targets, scale (billions of clips), and update frequency. Define success metrics like recall@k, mAP, and user engagement.
Propose a dual-encoder (two-tower) model: one for text and one for video, mapping to a shared embedding space. Use contrastive learning with hard negatives and consider temporal modeling for video.
Use a two-stage retrieval: approximate nearest neighbor (ANN) search (e.g., FAISS, ScaNN) for candidate generation, then a cross-encoder re-ranker for top-k. Discuss sharding, quantization, and distributed indexing for billion-scale.
Cover embedding generation at scale (batch processing, GPU inference), index updates (incremental vs. full rebuild), and latency optimization (caching, tiered storage).
Define offline metrics (recall, precision) and online A/B tests (CTR, watch time). Discuss how to handle cold-start and feedback loops, and how to monitor model drift.
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