Automatically generating theme-based folders by clustering media items in a semantic space

A system comprising:

• at least one processor;
• and
• a computer readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• generate tags and confidence scores for media items in a collection of media items utilizing a neural network, wherein the confidence scores indicate how strongly a given tag corresponds to a given media item;
• map the collection of media items to a semantic space based on the tags by generating semantic feature vectors for the media items of the collection of media items from the tags using a word to vector algorithm;
• cluster the collection of media items in the semantic space to create clusters by grouping similar semantic feature vectors;
• assign the media items of the collection of media items to the clusters;
• determine themes for the clusters based on the tags of the media items assigned to the clusters;
• generate a reverse index that maps the tags to associated media items with associated confidence scores;
• generate a media item navigation graphical user interface comprising the collection of media items organized into folders corresponding to the clusters and titled with the themes and a search query element;
• based on receiving a search query via the search query element, determine media items associated with the search query in the reverse index by:
• determining if the search query is in the reverse index;
• based on determining that the search query is not in the reverse index, determining a nearest term to the search query;
• and
• providing the media items associated with the nearest term based on the associated confidence scores;
• and
• provide, for display via the media item navigation graphical user interface, the media items associated with the search query.

In a digital medium environment for searching and presenting digital content, a computer-implemented method for recommending related media items, the computer-implemented method comprising:

• generating tags and confidence scores for media items in a collection of media items, wherein the confidence scores indicate how strongly a given tag corresponds to a given media item;
• mapping the collection of media items to a semantic space based on the tags;
• clustering the collection of media items in the semantic space to create clusters;
• determining themes for the clusters based on the tags of the media items in the clusters;
• generating a reverse index that maps the tags to associated media items with associated confidence scores;
• generating a media item navigation graphical user interface comprising the collection of media items organized by the themes and a search query element;
• receiving a search query via the search query element;
• based on determining that the search query is not in the reverse index, determining a nearest term to the search query;
• and
• providing media items associated with the nearest term based on corresponding confidence scores.