Question

A media company deploys a recommendation model that ranks articles for users. The model is retrained weekly using logged user clicks as ground-truth labels. Over several months, the team notices that recommendation diversity collapses—users increasingly see only a narrow set of popular topics, and engagement among niche-interest users declines. Data drift and skew monitoring show no anomalies in input feature distributions. What is the most likely root cause of this degradation?

Accepted Answer

A feedback loop: the model's own recommendations shape the click logs used for retraining, so labels are biased toward previously surfaced items, amplifying popularity bias over successive training cycles.

Answer

Training-serving skew caused by different preprocessing code paths between the training pipeline and the online serving container.

Answer

Concept drift in which the true relationship between features and user preferences has shifted independently of the model's behavior.

Answer

Numerical feature drift that the monitoring thresholds are configured too loosely to detect.