Abstract: A content provider system ranks media content items with respect to a particular user based on selection scores determined for each of the media content items. The selection scores may be determined using a particular model that calculates a predicted selection score based on feature values associated with the content item with respect to the particular user. The feature values may indicate properties of the media content item, the particular user, or the particular user's relationship with the content item, including information about the novelty of the media content item with respect to the user. The particular model may be trained with sample user consumption data points that represent various combinations of media content items and users. The data point information evaluated during the training of the particular model may cause the model to assign higher selection scores to content items that are novel in particular ways.

Abstract: According to a computer-implemented approach for renting items to customers, customers specify what items to rent using item selection criteria separate from deciding when to receive the specified items. According to the approach, customers provide item selection criteria to a provider provides the items indicated by the item selection criteria to customer over a delivery channel. The provider may be either centralized or distributed depending upon the requirements of a particular application. A “Max Out” approach allows up to a specified number of items to be rented simultaneously to customers. A “Max Turns” approach allows up to a specified number of item exchanges to occur during a specified period of time. The “Max Out” and “Max Turns” approaches may be used together or separately with a variety of subscription methodologies.