Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models used for generating content-item recommendations. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history, as well as modifying this score data using one or more biasing factors for generating result data. In addition, the techniques, devices, and systems may use this result data, along with received user input, for determining an order in which to present one or more content items to the user. For example, this may include determining which content items to recommend to a user and in which order to do so.