Abstract: A model assessment service is disclosed. The model assessment service may evaluate model metrics for an evaluation dataset using ground truth data and model predictions. The model assessment service may compare model performance by, among other things, comparing metric values against threshold values or against metric values of other models. Using a customizable configuration file, the model comparison may comprise different ways to compare models and different ways to evaluate specific metrics. As an example, the model assessment service can assess whether a new candidate model is to replace a deployed production model.

Abstract: Methods and systems for predicting relevant items to be presented to a user in an online environment. The methods and systems described herein generate models based on previous item selections to determine an overall model useable to generate item recommendations. In some examples, an item-level and category-level Hawkes process may be used, modeling a combination of an exponential distribution for short-history effects and Gaussian mixture probabilities to account for extended time period effects.