Determining a visual theme in a collection of media items

A computer-implemented method comprising:

• receiving as input to a trained machine-learning model, media items from a collection of media items associated with a user account, wherein media items are each associated with an associated timestamp and the media items that are captured within a predetermined time period are associated with an episode;
• generating, with the trained machine-learning model, output image embeddings for clusters of media items, wherein the media items in each cluster have a visual similarity and media items that have the visual similarity are nearer to each other in vector space than media items that are dissimilar, such that partitioning the vector space generates the clusters of media items and wherein each media item is associated with a location;
• selecting a subset of the clusters of media items based on:
• corresponding media items in each cluster having a visual similarity within a range of threshold visual similarity values;
• and
• corresponding associated timestamps such that the corresponding media items in the subset of the clusters of media items meet a temporal diversity criteria that excludes more than a first predetermined number of the corresponding media items from the episode;
• responsive to a number of the corresponding media items in the subset of clusters including more than a second predetermined number of media items, removing one or more of the corresponding media items based on location such that the subset of the clusters of media items meets a location diversity criteria;
• causing a user interface to be displayed that includes the subset of the clusters of media items;
• receiving aggregated feedback from users for aggregated subsets of clusters of media items;
• providing the aggregated feedback to a machine-learning model, wherein parameters of the machine-learning model are updated based on the aggregated feedback;
• and
• modifying the image embeddings for the clusters of media items using the parameters of the machine-learning model with the updated parameters.

A system comprising:

• a processor;
• and
• a memory coupled to the processor, with instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising:
• determining, based on pixels of images or videos from a collection of media items, image embeddings for clusters of media items such that the media items in each cluster have a visual similarity, wherein:
• each media item is associated with a location and an associated timestamp,
• media items captured within a predetermined time period are associated with an episode, and
• the collection of media items is associated with a user account;
• selecting a subset of the clusters of media items based on:
• corresponding media items in each cluster having the visual similarity within a range of threshold visual similarity values;
• and
• corresponding associated timestamps such that the corresponding media items in the subset of the clusters of media items meet a temporal diversity criteria that excludes more than a first predetermined number of the corresponding media items from the episode;
• responsive to a number of the corresponding media items in the subset of clusters including more than a second predetermined number of media items, removing one or more of the corresponding media items based on location such that the subset of the clusters of media items meets a location diversity criteria;
• causing a user interface to be displayed that includes the subset of the clusters of media items;
• receiving aggregated feedback from users for aggregated subsets of clusters of media items;
• providing the aggregated feedback to a machine-learning model, wherein parameters of the machine-learning model are updated based on the aggregated feedback;
• and
• modifying the image embeddings for the clusters of media items using the parameters of the machine-learning model with the updated parameters.