Marketplace Engineering Recsys Feature Engineering Best Practices

marketplace-personalisation

marketplace-search-recsys-planning

When to Apply

• 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

Rule Categories

audit-

firstp-

vision-

listing-

wizard-

derive-

quality-

prove-

Quick Reference

1. Asset Audit and Inventory (CRITICAL)

• audit-measure-coverage-before-modelling

  — reject fields below 80% coverage from the feature plan

• audit-sample-every-asset-type-end-to-end

  — pull 100 real instances through the real fetch path before planning

• audit-verify-rights-and-privacy-before-extraction

  — ToS, GDPR, consent, face blur before encoding

• audit-quantify-freshness-per-asset

  — age distribution + expiry + refresh bucket

• audit-separate-raw-assets-from-derived-features

  — raw immutable in object store, derived versioned in feature store

2. First-Principles Feature Decomposition (CRITICAL)

• firstp-start-from-the-decision-not-the-algorithm

  — decision first, sub-judgments second, tools last

• firstp-ask-what-signal-a-human-uses

  — interview 8-12 owners and sitters; features trace back to quotes

• firstp-tie-every-feature-to-a-specific-solution

  — no feature without a named i2i/u2i/u2u consumer

• firstp-prefer-directly-observed-over-learned

  — observed columns first, learned embeddings second

• firstp-reject-features-you-cannot-serve-at-inference

  — training-serving parity starts at design time

• firstp-kill-features-a-popularity-baseline-already-captures

  — correlation screen before registration

3. Image Feature Extraction (HIGH)

• vision-use-clip-for-zero-shot-listing-embeddings

  — zero-shot CLIP ships in a week

• vision-detect-room-types-before-detecting-amenities

  — room prior conditions the amenity threshold

• vision-quantify-image-quality-separately-from-content

  — blur, lighting, aesthetic as their own features

• vision-extract-per-object-counts-not-just-presence

  —

  n_bed = 4

  beats

  has_bed = true

• vision-pool-embeddings-across-a-listings-photo-set

  — pooled listing vector; per-photo stored alongside

• vision-fine-tune-on-your-domain-when-clip-underperforms

  — contrastive fine-tune only after zero-shot plateaus

4. Listing Text and Metadata Extraction (HIGH)

• listing-declare-categorical-fields-for-bounded-vocabularies

  — bounded vocab → categorical, validated on write

• listing-multi-hot-encode-amenity-lists

  — fixed amenity vocabulary → multi-hot vector

• listing-hash-geo-to-hierarchies-not-raw-lat-lon

  — H3 at multiple resolutions

• listing-embed-description-with-pretrained-sentence-encoder

  — all-MiniLM-L6-v2 for cheap semantic text features

• listing-extract-stay-duration-shape-not-just-length

  — bin + holiday overlap + flexibility, not raw day count

• listing-encode-pet-requirements-as-structured-triples

  —

  (species, count, special_needs)

  triples plus free text alongside

5. Sitter Wizard and Profile Extraction (HIGH)

• wizard-order-questions-by-information-gain

  — discriminative questions first, narrative last

• wizard-prefer-multiple-choice-over-free-text

  — categorical features by construction

• wizard-make-skips-genuine-and-log-them

  — skip is signal; defaults destroy it

• wizard-capture-experience-as-counts-and-dates

  — numbers, not adjectives; platform history overrides self-declaration

• wizard-separate-hard-constraints-from-soft-preferences

  — filters vs ranking features

6. Derived Similarity and Affinity (MEDIUM-HIGH)

• derive-precompute-i2i-nearest-neighbours-offline

  — ANN shelf built nightly, served from KV in <5ms

• derive-fuse-modalities-before-item-similarity

  — vision + text + structured, weighted and normalised

• derive-use-two-tower-for-user-item-affinity

  — dual encoder trained on interactions; ANN-retrieval-ready

• derive-score-u2u-as-symmetric-mutual-fit

  —

  min(P(owner), P(sitter))

  ; one-sided scoring produces wasted requests

• derive-decompose-affinity-into-interpretable-subscores

  — fit/safety/logistics/price subscores + blend

• derive-cache-user-embedding-with-short-ttl

  — session-level cache, 60-300s TTL

7. Feature Quality and Governance (MEDIUM-HIGH)

• quality-version-feature-definitions-in-one-registry

  — one name, one implementation, one owner

• quality-serve-training-and-inference-from-one-store

  — feature store as the single source of truth

• quality-gate-features-on-coverage-and-drift

  — coverage floor + PSI alarm

• quality-scrub-pii-before-features-leave-secure-zone

  — face blur and regex scrubbing before encoding

• quality-freeze-feature-schemas-per-model-version

  — schema hash pinned to model artifact

8. Incremental Rollout and Value Proof (MEDIUM)

• prove-ship-one-feature-at-a-time

  — one feature, one experiment, one decision

• prove-measure-lift-against-feature-ablated-variant

  — ablation isolates the feature from incidental changes

• prove-kill-features-that-dont-earn-maintenance

  — quarterly kill review on attributed lift

• prove-dedicate-random-exploration-slice-to-new-features

  — 3-5% slice catches offline-close-to-tied winners

• prove-retain-feature-free-baseline-permanently

  — popularity baseline as drift anchor

Discovering New Features

• references/playbooks/discovering.md

  — Discover new features from raw marketplace assets: a seven-step workflow that starts with an asset audit and a decision decomposition and ends with a shipped ablation A/B against a feature-ablated baseline. Use when the task is "what should we build next?" rather than "fix this specific feature."

How to Use

• Read

  references/_sections.md

  for category structure and cascade rationale
• Read

  gotchas.md

  for accumulated diagnostic lessons before suggesting interventions
• Read

  references/playbooks/discovering.md

  to plan a new feature discovery cycle
• Read individual rule files under

  references/

  when a specific task matches the rule title
• Use

  assets/templates/_template.md

  to author new rules as the skill grows

Related Skills

• marketplace-personalisation

  — Post-extraction personalisation on AWS Personalize: event tracking, schema design, two-sided matching, cold start, feedback loops. Hand off once your features are in the store and you are ready to train a ranker.

• marketplace-search-recsys-planning

  — OpenSearch retrieval planning: query understanding, index design, ranking, search-plus-recs blending. Hand off when the bottleneck is retrieval rather than feature availability.

• marketplace-pre-member-personalisation

  — Pre-member journey from anonymous visit to paid membership: anonymous signal inference, onboarding intent capture, pre-member measurement. Hand off at the paid-member boundary.

Reference Files