Synthesizing visualizations for content collections
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing synthetic visualizations representative of content collections within a content management system. In some cases, the disclosed systems generate a synthetic visualization based on content features that indicate relevance of content items with respect to a user account to emphasize more relevant content items within the synthetic visualization and/or to represent descriptive content attributes of the content items. For example, the disclosed systems can generate a synthetic phrase that represents a content collection and can further generate a synthetic visualization from the synthetic phrase utilizing a synthetic visualization machine learning model.
1. A method comprising:
identifying a content collection comprising a plurality of content items within a content management system;
generating a set of content features representing the plurality of content items within the content collection;
generating, by synthesizing a visual representation of the plurality of content items from the set of content features into a single interface element, a synthetic visualization comprising the single interface element representative of the content collection utilizing a synthetic visualization machine learning model; and
providing the synthetic visualization for display as representative of the content collection.
2. The method of claim 1 , wherein generating the set of content features comprises determining one or more of:
descriptive features representing content attributes of the plurality of content items; or
relevance features indicating a measure of relevance of the plurality of content items to a user account within the content management system.
3. The method of claim 1 , wherein generating the synthetic visualization comprises synthesizing a visual representation of the plurality of content items by generating a synthetic digital image representing the plurality of content items by utilizing the synthetic visualization machine learning model trained to generate visual representations based on sets of content features.
4. The method of claim 1 , further comprising generating, from the set of content features, a synthetic phrase representing the content collection and describing the plurality of content items within the content collection.
5. The method of claim 4 , wherein generating the synthetic phrase representing the content collection comprises utilizing a synthetic phrase machine learning model trained to generate synthetic phrases from content features.
6. The method of claim 4 , wherein generating the synthetic visualization comprises generating a visual representation of the synthetic phrase representing the content collection.
7. The method of claim 1 , further comprising:
generating a relevance profile for a user account based on historical behavior of the user account within the content management system; and
generating the set of content features to indicate a correspondence between the relevance profile of the user account and the plurality of content items.
8. A system comprising:
at least one processor; and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
identify a content collection comprising a plurality of content items within a content management system;
generate a set of content features representing the plurality of content items within the content collection;
generate, by synthesizing a visual representation of the plurality of content items from the set of content features into a single interface element, a synthetic visualization comprising the single interface element representative of the content collection utilizing a synthetic visualization machine learning model; and
provide the synthetic visualization for display as representative of the content collection.
9. The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the synthetic visualization for the content collection by:
generating a first visual representation for a first content item within the content collection;
generating a second visual representation for a second content item within the content collection; and
combining the first visual representation and the second visual representation to form the synthetic visualization.
10. The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the synthetic visualization for display as representative of the content collection by providing the synthetic visualization for display as an interface element representing the content collection within a user interface of the content management system displayed on a client device.
11. The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
utilize a ranking model to rank the plurality of content items according to relevance with respect to a user account within the content management system; and
generate the synthetic visualization based on ranking the plurality of content items according to relevance.
12. The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
determine one or more keywords corresponding to the plurality of content items within the content collection; and
combine the one or more keywords into a synthetic phrase representing the content collection.
13. The system of claim 12 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the synthetic visualization for the content collection from the synthetic phrase utilizing the synthetic visualization machine learning model trained to generate synthetic visualizations from synthetic phrases.
14. The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
detect a change to the plurality of content items within the content collection; and
update the synthetic visualization for the content collection based on the change to the plurality of content items.
15. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to:
identify a content collection comprising a plurality of content items within a content management system;
generate a set of content features representing the plurality of content items within the content collection;
generate, by synthesizing a visual representation of the plurality of content items from the set of content features into a single interface element, a synthetic visualization comprising the single interface element representative of the content collection utilizing a synthetic visualization machine learning model; and
provide the synthetic visualization for display as representative of the content collection.
16. The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate the synthetic visualization for the content collection by:
identifying a visual representation of the plurality of content items within the content collection; and
modifying the visual representation of the plurality of content items to represent the content collection as a whole.
17. The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
detect user account behavior associated with the content collection within the content management system; and
update the synthetic visualization for the content collection based on the user account behavior.
18. The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate a synthetic phrase representing the content collection by:
utilizing a synthetic phrase machine learning model to classify the plurality of content items into content classifications corresponding to content labels; and
generating the synthetic phrase from the content labels of the plurality of content items.
19. The non-transitory computer readable medium of claim 18 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate the synthetic visualization from the synthetic phrase utilizing the synthetic visualization machine learning model.
20. The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
identify an additional user account with access to the plurality of content items within the content collection; and
generate an additional synthetic visualization to represent the content collection for the additional user account, wherein the additional synthetic visualization differs in appearance from the synthetic visualization.