Marketplace Engineering Recsys Feature Engineering Best Practices

Comprehensive first-principles guide for deriving usable recommender features from the raw assets of a two-sided trust marketplace — listing photos, owner-supplied listing metadata, and sitter wizard responses — for item-to-item, user-to-item, and user-to-user solutions. Contains 44 rules across 8 categories ordered by cascade impact on the feature-engineering lifecycle, plus one playbook that composes the rules into an end-to-end feature discovery workflow.

This skill is the upstream precursor to marketplace-personalisation (AWS Personalize) and marketplace-search-recsys-planning (OpenSearch retrieval). Those skills treat features as inputs they already have; this skill is about deciding what features to build from the raw assets, which decisions they serve, and how to prove each one is worth its maintenance cost.

When to Apply

Reference this skill when:

• Planning what to extract from listing photos, descriptions, or amenity lists to power i2i similarity or u2i ranking
• Designing or revising the sitter onboarding wizard with recsys features as the primary output
• Deciding whether to build a vision embedding pipeline, a text encoder, or neither — and in what order
• Composing existing base features into item-to-item, user-to-item, or user-to-user scoring
• Auditing an existing feature store for coverage, drift, PII, duplication, or orphan features
• Choosing a ship/kill criterion for a new recsys feature and designing the ablation A/B test
• Answering the question: "we want to improve the similar-homes shelf — what feature should we build?"

Setup