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EXHIBIT FDATA · MACHINE LEARNING — CASE STUDY

RECO ENGINE

Collaborative filtering and matrix factorization that rank listings by user behavior — converting what users do into ranking logic they can act on.

ORGANIZATION

INDEPENDENT BUILD

PERIOD

2024 — 2025

ROLE

MODELING & RANKING LOGIC — SOLO

STACK

SCIKIT-LEARN · PANDAS

01 — THE BRIEF

Users tell you what they want through behavior long before they'd articulate it in a search filter. The problem is turning scattered interactions into an ordering that's actually useful.

The Reco Engine does that with proven recommendation math: collaborative filtering and matrix factorization over user-behavior data, producing ranked listings instead of an unordered pile.

02 — OPERATING CONSTRAINTS

  • Behavior data is sparse — most users touch few listings, so the model must generalize from little signal.
  • The output had to be ranking logic a product can act on, not a research notebook.
  • Interpretable, tunable methods over black-box novelty.

03 — SYSTEM STRUCTURE

BEHAVIOR DATA PREP

Pandas — user-listing interactions structured into matrices the models can learn from.

COLLABORATIVE FILTERING

Users who behave alike predict each other's interests.

MATRIX FACTORIZATION

Scikit-learn — latent factors filling the gaps sparse behavior leaves behind.

RANKED OUTPUT

An ordered listing feed — ranking logic, ready for a product surface.

04 — KEY DECISIONS

DECISION 01

Proven math over fashionable models

Collaborative filtering and matrix factorization are the recommendation industry's load-bearing walls — understood, tunable, and defensible in review.

DECISION 02

Behavior over declared preference

What users click outranks what they say — the engine learns from actions.

DECISION 03

Ship ranking logic, not a paper

The deliverable is an ordering a product can use — decision output, same as every build in this file.

05 — OUTCOME

Behavior data became ranking logic users can act on.

  • · Pairs with Exhibit E — clean listings in, personalized ordering out.

CROSS-EXAMINATION

The full walkthrough — code, schema, trade-offs — happens live. Fifteen minutes, no slides.

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