One or more embodiments of a content system provide machine-learned storage location recommendations for storing content items. Specifically, an online content management system can train a machine-learning model to identify a storage pattern from previously stored content items in a plurality of storage locations corresponding to a user account of a user. Training the machine-learning model includes training a plurality of classifiers for the plurality of storage locations. The online content management system uses the classifiers to determine whether a content item is similar to the content items in any of the storage locations, and based on the output of the classifiers, provides graphical elements indicating recommended storage locations within a graphical user interface. The user can select a graphical element to move the content item to the corresponding storage location.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: determining, by at least one processor, characteristics of a plurality of content items stored in a plurality of storage locations associated with a user account; training, by the at least one processor and using the characteristics of the plurality of content items, a machine-learning model to classify new content items based on characteristics associated with the new content items; determining, by the at least one processor and using the machine-learning model, that a group of new content items in a temporary storage location comprise a shared characteristic; determining, by the at least one processor and based on the shared characteristic, a recommended storage location for the group of new content items; and moving, in response to a user input selecting the recommended storage location, the group of new content items from the temporary storage location to the recommended storage location. 2. The method as recited in claim 1, wherein training the machine-learning model comprises training a plurality of classifiers for the plurality of storage locations associated with the user account. 3. The method as recited in claim 2, further comprising: determining, for a first storage location from the plurality of storage locations, a first characteristic that a plurality of content items in the first storage location have in common; determining, for a second storage location from the plurality of storage locations, a second characteristic that a plurality of content items in the second storage location have in common; and training, for the machine-learning model, a first classifier based on the first characteristic and a second classifier based on the second characteristic. 4. The method as recited in claim 3, wherein: determining that the group of new content items in the temporary storage location comprise the shared characteristic comprises determining, using the machine-learning model, that each content item in the group of new content items corresponds to the first classifier associated with the first characteristic; and determining the recommended storage location for the group of new content items comprises determining the first storage location as the recommended storage location. 5. The method as recited in claim 2, further comprising applying, for a classifier from the plurality of classifiers, a weight to each characteristic from a plurality of characteristics of a plurality of content items stored in a storage location from the plurality of storage locations. 6. The method as recited in claim 2, further comprising: scoring, using the plurality of classifiers, the plurality of storage locations for the group of new content items; and providing, for display within a user interface, a graphical element indicating a recommendation to move the group of new content items from the temporary storage location to a storage location with a highest score from the plurality of storage locations. 7. The method as recited in claim 6, further comprising providing, for display within the user interface, a subset of storage locations from the plurality of storage locations in an order based on scores associated with the plurality of storage locations. 8. The method as recited in claim 7, further comprising determining that each storage location in the subset of storage locations meets a predetermined score threshold. 9. The method as recited in claim 1, further comprising: identifying a parent location of the recommended storage location; and providing an additional recommendation to store the group of new content items in a new storage location within the identified parent location. 10. The method as recited in claim 1, further comprising: storing the group of new content items in the temporary storage location with a plurality of additional content items in response to the group of new content items being added to the user account during a first login session; and providing the recommended storage location for the group of new content items during a second login session. 11. A system comprising: at least one processor; and a non-transitory computer readable storage medium comprising instructions that, when executed by the at least one processor, cause the system to determine characteristics of a plurality of content items stored in a plurality of storage locations associated with a user account; train, based on the characteristics of the plurality of content items, a machine-learning model to classify new content items based on characteristics associated with the new content items; determine, using the machine-learning model, that a group of new content items in a temporary storage location comprise a shared characteristic; determine, based on the shared characteristic, a recommended storage location for the group of new content items; and move, in response to a user input selecting the recommended storage location, the group of new content items from the temporary storage location to the recommended storage location. 12. The system as recited in claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to train the machine-learning model by training a plurality of classifiers for the plurality of storage locations associated with the user account. 13. The system as recited in claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that the group of new content items in the temporary storage location comprise the shared characteristic by determining, using the machine-learning model, that each content item in the group of new content items corresponds to a classifier associated with the shared characteristic from the plurality of classifiers; and determine the recommended storage location for the group of new content items by identifying a storage location corresponding to the classifier associated with the shared characteristic. 14. The system as recited in claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: score, using the machine-learning model, the plurality of storage locations for the group of new content items; and provide, for display within a user interface, a graphical element indicating a recommendation to move the group of new content items from the temporary storage location to a storage location with a highest score from the plurality of storage locations. 15. The system as recited in claim 14, further comprising instructions that, when executed by the at least one processor, cause the system to provide, for display within the user interface, a subset of storage locations from the plurality of storage locations in an order based on scores associated with the plurality of storage locations, wherein each storage location in the subset of storage locations meets a predetermined score threshold. 16. The system as recited in claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: identify a parent location of the recommended storage location; and provide an additional recommendation to store the group of new content items in a new storage location within the identified parent location. 17. The system as recited in claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: store the group of new content items in the temporary storage location with a plurality of additional content items during a first login session for the user account; and provide the recommended storage location for the group of new content items during a second login session for the user account. 18. A non-transitory computer readable storage medium comprising instructions that, when executed by at least one processor, cause a computer system to: determine characteristics of a plurality of content items stored in a plurality of storage locations associated with a user account; train, based on the characteristics of the plurality of content items, a machine-learning model to classify new content items based on characteristics associated with the new content items; determine, using the machine-learning model, that a group of new content items in a temporary storage location comprise a shared characteristic; determine, based on the shared characteristic, a recommended storage location for the group of new content items; and move, in response to a user input selecting the recommended storage location, the group of new content items from the temporary storage location to the recommended storage location. 19. The non-transitory computer readable storage medium as recited in claim 18, further comprising instructions that, when executed by at least one processor, cause the computer system to: determine that the group of new content items in the temporary storage location comprise the shared characteristic by determining, using the machine-learning model, that each content item in the group of new content items corresponds to a trained classifier associated with the shared characteristic; and determine the recommended storage location for the group of new content items by identifying a storage location corresponding to the trained classifier associated with the shared characteristic. 20. The non-transitory computer readable storage medium as recited in claim 18, further comprising instructions that, when executed by at least one processor, cause the computer system to: score, using the machine-learning model, the plurality of storage locations for the group of new content items; and provide, for display within a user interface, a graphical element indicating a recommendation to move the group of new content items from the temporary storage location to a storage location with a highest score from the plurality of storage locations.