Build candidate retrieval and ranking experiments from interaction data. Compare recommendations with a popularity baseline and examine cold-start behavior, cov
Last reviewed: 2026-10-03
Recommendation systems select candidates from a catalog and order them for a particular context. Retrieval reduces the candidate set; ranking evaluates that smaller set more closely. The TensorFlow Recommenders retrieval tutorial demonstrates learning user and item representations from interaction data.
For a proposed retail or media exercise, compare your model with a popularity baseline. Separate earlier interactions from later evaluation activity, inspect new-user and new-item behavior, and report coverage as well as ranking quality. An offline ranking score does not establish commercial impact.
They serve different stages. Retrieval selects a manageable set from the full catalog; ranking orders that set using the chosen objective and available context.