Bidirectional LSTM sequence encoder with padding-aware attention and a next-item classification head. The production artifact includes architecture parameters, item vocabulary, dataset ID, ranking metrics, version, creation time, and checksums.
Ranking known catalogue items for users with at least one known recent interaction. It is not intended for safety-critical decisions, eligibility, employment, credit, health, or other high-impact domains.
Report Recall@K, NDCG@K, MRR@K, catalogue coverage, and the same metrics for a popularity baseline. Add novelty, diversity, calibration, popularity bias, and cold-start cohorts before a real launch.
The dense output head scales linearly with catalogue size. Unknown items are rejected from the sequence. Historical interactions may encode exposure and popularity bias. Offline ranking metrics do not prove causal user or business impact.
A candidate needs a versioned dataset, reproducible configuration, compatible bundle, quality no worse than the declared baseline, target-environment evidence, and an approved rollback plan.