Computer-implemented methods, systems, and computer-readable media for a categorized favorite food user generated content/social network are described.

A specific item is identified to suggest a replacement therefor to a user. A set of candidate replacement items for the specific item is determined. For at least one of the candidate replacement items, an expiration score is determined based on expiration information associated with the item. A replacement score for the candidate replacement item is determined by inputting the determined expiration score as a feature into a machine learning model that is trained using features of historical samples of candidate replacement items suggested as a replacement to users and the replacement suggestion being accepted by the users. One or more of the candidate replacement items is selected based on respective replacement scores as one or more suggested replacement items. A graphical user interface of a client device of the user is caused to display the one or more suggested replacement items as the replacement for the specific item.

A system includes a monitoring module configured to determine at least one of a location and a route of a vehicle, and a recommendation module configured to identify a plurality of service stations based on the at least one of the location and the route of the vehicle. The monitoring module is configured to compare a preference of a first user of the vehicle to preference data related to a second user of another vehicle, predict a preferred service station of the plurality of service stations based on the comparing, and present a recommendation to the first user, the recommendation indicating the preferred service station.