Content-first collection generation

A system comprising:

• one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• obtaining a collection of content items, one or more of the content items including an image and associated metadata;
• generating a collection of attributes for one or more content items of the collection of content items by analyzing the image and the associated metadata, the generating including extracting attributes from one or more content items using a vision language model trained to generate structured attributes from content items and associated metadata;
• assigning one or more attributes to one or more content item;
• generating, using a machine learning model, a plurality of queries as a model output from an input comprising the collection of attributes;
• and
• populating a plurality of collections of content items, one or more of the collections having a topic corresponding to one of the generated plurality of queries, wherein populating the collections comprises evaluating a relevance of each query-content item pairs based on an overlap between attributes associated with the query and attributes assigned to the content item.

One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• obtaining a collection of content items, one or more of the content items including an image and associated metadata;
• generating a collection of attributes for one or more content items of the collection of content items by analyzing the image and the associated metadata, the generating including extracting attributes from one or more content items using a vision language model trained to generate structured attributes from content items and associated metadata;
• assigning one or more attributes to one or more content items;
• generating, using a machine learning model a plurality of queries as a model output from an input comprising the collection of attributes;
• and
• populating a plurality of collections of content items, one or more of the collections having a topic corresponding to one of the generated plurality of queries, wherein populating the collections comprises evaluating a relevance of query-content item pairs based on an overlap between attributes associated with the query and attributes assigned to the content item.