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marketplace-recsys-feature-engineering

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Feature engineering for marketplace recommenders — what to extract from raw marketplace assets (listing photos, owner-entered listing metadata, sitter wizard responses) to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders. Covers asset auditing, first-principles feature decomposition, vision-feature extraction (CLIP, room-type, amenities, aesthetics), listing text and metadata encoding, sitter wizard design, derived-composition patterns for i2i / u2i / u2u (ANN shelves, two-tower, mutual-fit), feature quality governance (training-serving parity, drift, PII), and incremental value proof (ablation A/B, kill reviews, feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.

skills/.experimental/marketplace-recsys-feature-engineering/SKILL.md

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# 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