ML System Design
What improvements would you add after the baseline real-estate search works: user context, visual embeddings, VLMs, quality models or richer item representations?
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Improve item representation with room/photo classification, image quality, extracted amenities and multimodal embeddings; add user/session context cautiously and measure gains by slices.
Полный разбор
After a baseline works, richer item representations often pay off. For photos, classify room type, detect low-quality or irrelevant images, select the best cover photo, extract visible amenities and compute visual embeddings. For text, normalize attributes and enrich descriptions with extracted structured facts.
User and session context can improve relevance: previous searches, viewed listings, favorites, price tolerance, preferred districts, device, travel dates and transaction type. Use it carefully so the model does not override explicit query constraints.
VLMs can help with long-tail visual attributes such as "bright kitchen", "renovated bathroom" or "view of park", but they need evaluation for hallucinations and latency. Many VLM outputs are better used offline to enrich listings rather than online per query. Every improvement should be evaluated by query type, property type, geo and freshness because image-heavy gains may not generalize.
Теория
In search, richer item understanding is useful only if it improves retrieval/ranking without breaking explicit intent.
Типичные ошибки
- Use user personalization to override a clear query.
- Run expensive VLM inference online for every query-item pair.
- Ignore photo quality and room-type classification.
Как отвечать на собеседовании
- Suggest offline VLM enrichment first.
- Separate explicit query intent from personalization.