A method implemented by one or more processors, the method comprising: receiving a plurality of items of content to be recommended to a user of a client device; for each item of content to be recommended for the user: determining a plurality of candidate endorsements corresponding to the item of content by processing the item of content using a plurality of nominators including a variety of types of nominators, each nominator generating one of the candidate endorsements, wherein each type of nominator generates candidate endorsements based on a distinct corpus of information related to the item of content, and where each candidate endorsement provides a natural language description of information relating to the item of content; determining a score for each candidate endorsement by processing the plurality of candidate endorsements corresponding to the item of content using a ranking agent, where the score is indicative of a likelihood the user will interact with the item of content based on the candidate endorsement, and where the ranking agent determines the score uniformly across the plurality of nominators; selecting an endorsement from the plurality of candidate endorsements to present with the item of content based on the score of each candidate endorsement corresponding to the item of content; and rendering the plurality of items of content and the selected endorsements corresponding to the plurality of items for the user via one or more user interface output devices of the client device. receiving a plurality of items of content to be recommended to a user of a client device; for each item of content to be recommended for the user: determining a plurality of candidate endorsements corresponding to the item of content by processing the item of content using a plurality of nominators including a variety of types of nominators, each nominator generating one of the candidate endorsements, wherein each type of nominator generates candidate endorsements based on a distinct corpus of information related to the item of content, and where each candidate endorsement provides a natural language description of information relating to the item of content; determining a score for each candidate endorsement by processing the plurality of candidate endorsements corresponding to the item of content using a ranking agent, where the score is indicative of a likelihood the user will interact with the item of content based on the candidate endorsement, and where the ranking agent determines the score uniformly across the plurality of nominators; selecting an endorsement from the plurality of candidate endorsements to present with the item of content based on the score of each candidate endorsement corresponding to the item of content; and determining a plurality of candidate endorsements corresponding to the item of content by processing the item of content using a plurality of nominators including a variety of types of nominators, each nominator generating one of the candidate endorsements, wherein each type of nominator generates candidate endorsements based on a distinct corpus of information related to the item of content, and where each candidate endorsement provides a natural language description of information relating to the item of content; determining a score for each candidate endorsement by processing the plurality of candidate endorsements corresponding to the item of content using a ranking agent, where the score is indicative of a likelihood the user will interact with the item of content based on the candidate endorsement, and where the ranking agent determines the score uniformly across the plurality of nominators; selecting an endorsement from the plurality of candidate endorsements to present with the item of content based on the score of each candidate endorsement corresponding to the item of content; and rendering the plurality of items of content and the selected endorsements corresponding to the plurality of items for the user via one or more user interface output devices of the client device.