The present disclosure is directed to collaborative feature learning using social media data. For example, a machine learning system may identify social media data that includes user behavioral data, which indicates user interactions with content item. Using the identified social user behavioral data, the machine learning system may determine latent representations from the content items. In some embodiments, the machine learning system may train a machine-learning model based on the latent representations. Further, the machine learning system may extract features of the content item from the trained machine-learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for collaborative feature learning using unsupervised social media data, the method comprising: identifying user behavioral data from a social media system in relation to content items in the social media system; determining, by at least one processor, latent representations from the content items from the identified user behavioral data; and learning features of the content items based on the latent representations determined from the content items. 2. The method of claim 1, wherein the user behavioral data comprises implicit behavioral correlations between users and the content items. 3. The method of claim 1, further comprising extracting content item features from the learned features of the content item using machine learning. 4. The method of claim 1, wherein determining the latent representations from the content items from the identified user behavioral data comprises deriving latent features of the content items from the identified user behavioral data. 5. The method of claim 3, wherein learning the features of the content items based on the latent representations determined from the content items comprises training a neural network using the derived latent features of the content items to learn the features of the content item. 6. The method of claim 3, further comprising: grouping the derived latent features of the content items into a plurality of clusters; and wherein learning features of the content items comprises learning features of the content items based on the plurality of clusters. 7. The method of claim 1, wherein the user behavioral data from the social media system in relation to the content items in the social media system comprises at least one of views, appreciations, or ownership. 8. The method of claim 1, further comprising: receiving a request to perform a machine learning application on an additional set of content items; identifying features of one or more content items of the additional set of content items based on the features learned from the content items in the social media system; and performing the machine learning application using the identified features of the one or more content items of the additional set of content items. 9. The method of claim 8, wherein the additional set of content items are not part of the content items in the social media system. 10. The method of claim 8, wherein the request to perform a machine learning application comprises receiving a request to identify content items from the additional set of content item similar to a query content item. 11. A method for collaborative feature learning using unsupervised social media data comprising: identifying user behavioral data from a social media system in relation to content items in the social media system; decomposing the user behavioral data to identify latent factors of the content items; grouping the latent factors of the content items into a plurality of clusters; training a machine-learning model based on the plurality of clusters; and extracting content item features from the machine-learning model trained based on the plurality of clusters. 12. The method of claim 11, wherein the user behavioral data from the social media system in relation to the content items in the social media system indicate when a particular user interacted with one or more content items of the content items. 13. The method of claim 11, further comprising removing user behavioral data that is above a first threshold number of user interactions or that is below a second threshold number of user interactions. 14. The method of claim 11, wherein grouping the latent factors of the content items into the plurality of clusters comprises applying k-means clustering to the latent factors of the content items to group the latent factors into the plurality of clusters. 15. The method of claim 11, further comprising creating pseudo classes for the content items using the plurality of clusters, wherein training the machine-learning model based on the plurality of clusters comprises training a neural network based on the pseudo classes for the content items. 16. The method of claim 11, further comprising composing a user-interaction matrix using the user behavioral data from the social media system in relation to the content items in the social media system, wherein the user-interaction matrix comprises entries that indicate interactions between each user of the social media system and each content item of the content items. 17. The method of claim 16, further comprising: identifying one or more missing entries in the user-interaction matrix, the one or more missing entries indicating that a particular user did not interact with a particular content item of the content items; and proportionally populating, for each user, the one or more missing entries in the user-interaction matrix with positive and negative sample indications. 18. The method of claim 16, further comprising: performing regularization on non-missing entries of the user-interaction matrix; and optimizing the non-missing entries of the user-interaction matrix using asynchronous stochastic gradient descent. 19. The method of claim 11, wherein the user behavioral data comprises implicit behavioral correlations between users and the content items. 20. A system for collaborative feature learning using unsupervised social media data comprising: at least one processor; at least one non-transitory computer readable storage medium storing instructions thereon, that, when executed by the at least one processor, cause the system to: identify user behavioral data from a social media system in relation to content items in the social media system; decompose the user behavioral data to identify latent factors of the content items; group the latent factors of the content items into a plurality of clusters; train a machine-learning model based on the plurality of clusters; and extract content item features from the machine-learning model trained based on the plurality of clusters.