Embodiments of the present disclosure include a computer-implemented method and system for determining when to retrain an individual-item model within a recommendation engine. The computer-implemented method includes defining a consumer feature vector having attributes of historical consumers that impact an individual-item model. The computer-implemented method further includes calculating a historical feature vector relating to the historical consumers. The computer-implemented method also includes determining a retraining threshold for the individual-item model and calculating a new feature vector relating to new consumers. The new feature vector containing new attribute values of the new consumers and defined by the consumer feature vector. The computer-implemented method further includes determining a distance between the historical feature vector and the new feature vector and retraining the individual-item model upon determining that the distance between the historical feature vector and the new feature vector exceeds the retraining threshold.