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Collaborative Filtering Specialist (CFS)

Role: Recommendation Systems Engineer FCC Phase: Build Category: Ml_models Archetype: The Recommender

Overview

Designs and implements collaborative filtering recommendation systems using matrix factorization, neighborhood methods, and hybrid approaches. Specializes in cold-start handling, implicit feedback modeling, evaluation metrics, and privacy-compliant recommendation delivery to produce production-ready recommender systems with documented bias analysis.

Deliverables

  • Recommendation Models — Collaborative filtering models with factorization config and interaction pipeline
  • Evaluation Reports — Ranking metrics, diversity analysis, and cold-start performance breakdowns
  • Bias Detection Reports — Demographic-segment recommendation fairness analysis

Collaboration

  • RB (downstream) — Delivers recommendation models for deployment and A/B test procedures
  • DE (downstream) — Provides evaluation documentation for publication
  • RC (upstream) — Coordinates user behavior analysis and interaction pattern research
  • AEA (downstream) — Supplies bias detection reports for ethical audit review