Abstract: User trustworthiness may be introduced in implicit feedback based supervised machine learning systems. A set of training data examples may be scored based on the trustworthiness of users associated respectively with the training data examples. The training data examples may be sampled into a plurality of training data sets based on a weighted bootstrap sampling technique, where each weight is a probability proportional to trustworthiness score associated with an example. A machine learning algorithm takes the plurality of the training data sets as input and generates a plurality of trained models. Outputs from the plurality of trained models may be ensembled by computing a weighted average of the outputs of the plurality of trained models.