Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for iterative collaborative feature learning usable for accurately matching image content in queries to corresponding image results, the method comprising: obtaining, by a processor, a data set that includes data indicating whether a user providing a text query clicked on a content item resulting from a search of the text query; populating, by the processor, missing data in the data set; determining, by the processor, a first latent factor of the content item from the data set, wherein the first latent factor includes information about a feature of the content item; learning, by the processor, a first feature of the content item based on the first latent factor; iteratively learning, by the processor, a second feature of the content item by adding the first feature into the data set to decrease a sparsity of the data set, wherein each iteration comprises: decomposing the data set to identify a second latent factor of the content item based on the data set, grouping the first latent factor and the second latent factor into a cluster, and learning the second latent factor of the content item based on the cluster; and training a neural network usable matching content items in queries by learning to classify a plurality of content items based at least partially on the first or the second feature of the content item. 2. The method of claim 1, further comprising: receiving, by the processor, data associated with a query content item; determining, by the processor, a latent factor of the query content item; learning, by the processor, a feature of the query content item; and outputting, by the processor, a recommendation indicating a recommended content item, wherein the recommended content item is similar to the query content item. 3. The method of claim 1, further comprising: determining, by the processor, whether the user providing the text query clicked on the content item resulting from the search of the text query; and outputting, by the processor, a recommendation to the user indicating a recommended content item, wherein the recommended content item is similar to the content item clicked on by the user. 4. The method of claim 1, wherein populating missing data in the data set includes: determining, by the processor, an amount of missing data in the data set; generating, by the processor, derived data for the data set based on the amount of missing data being below a threshold amount of missing data; and populating, by the processor, missing data in the data set by replacing the missing data with the derived data. 5. The method of claim 1, wherein the data set is a data matrix and wherein determining the first latent factor or the second latent factor of the content item includes: decomposing, by the processor, the data matrix into a plurality of data matrices that include data associated with the first latent factor or the second latent factor; and deriving the first latent factor or the second latent factor from data in the plurality of data matrices. 6. The method of claim 5, further comprising: grouping, by the processor, the first latent factor and the second latent factor into a plurality of clusters and wherein learning the first feature or the second feature of the content item includes learning the first feature or the second feature based on the plurality of clusters. 7. The method of claim 1, wherein obtaining the data set includes: determining by the processor, that outlier data from the data set includes data in the data set that is above or below a threshold associated with an accuracy with which the neural network classifies the plurality of content items; removing, by the processor, the outlier data from the data set based on the data in the data set being above or below the threshold. 8. A system comprising: a processing device; and a non-transitory computer-readable medium communicatively coupled to the processing device, wherein the processing device is configured to perform operations comprising: obtaining a data set that includes data indicating whether a user providing a text query clicked on a content item resulting from a search of the text query; populating missing data in the data set; determining a first latent factor of the content item from the data set, wherein the first latent factor includes information about a feature of the content item; learning a first feature of the content item based on the first latent factor; iteratively learning a second feature of the content item by adding the first feature into the data set to decrease a sparsity of the data set, wherein each iteration comprises: decomposing the data set to identify a second latent factor of the content item based on the data set, grouping the first latent factor and the second latent factor into a cluster, and learning the second latent factor of the content item based on the cluster; and training a neural network usable matching content items in queries by learning to classify a plurality of content items based at least partially on the first or the second feature of the content item. 9. The system of claim 8, wherein the processing device is further configured to train the neural network to: to learn feature of a plurality of content items and classify the plurality of content items based on the learned features. 10. The system of claim 8, wherein the processing device is further configured to: receive data associated with a query content item; determine a latent factor of the query content item; learn a feature of the query content item; and output a recommendation indicating a recommended content item, wherein the recommended content item is similar to the query content item. 11. The system of claim 8, wherein the processing device is further configured to: determine an amount of missing data in the data set; generate derived data for the data set based on the amount of missing data being below a threshold amount of missing data; and populate missing data in the data set by replacing the missing data with the derived data. 12. The system of claim 9, wherein the processing device is further configured to: remove outlier data from the data set to improve an accuracy with which the trained neural network classifies the plurality of content items, wherein outlier data includes data in the data set that is above or below a threshold. 13. The system of claim 8, wherein the data set is a data matrix and the processing device is further configured to: decompose the data matrix into a plurality of data matrices that include data associated with the first latent factor or the second latent factor and deriving the first latent factor or the second latent factor from data in the plurality of data matrices. 14. The system of claim 13, wherein the processing device is further configured to: group the first latent factor and the second latent factor into a plurality of clusters and learn the first feature or the second feature based on the plurality of clusters. 15. A non-transitory computer-readable medium storing program code executable by a processor for iterative collaborative feature learning usable for accurately matching image content in queries to corresponding image results, the program code comprising: program code for obtaining, by the processor, a data set that includes data indicating whether a user providing a text query clicked on a content item resulting from a search of the text query; program code for determining, by the processor, an amount of missing data in the data set; program code for generating, by the processor, derived data for the data set based on the amount of missing data being below a threshold amount of missing data; program code for populating, by the processor, the missing data in the data set using the derived data; program code for determining, by the processor, a first latent factor of the content item from the data set, wherein the first latent factor includes information about a feature of the content item; program code for learning, by the processor, a first feature of the content item based on the first latent factor; program code for iteratively learning, by the processor, a second feature of the content item by adding the first feature into the data set, wherein each iteration comprises: decomposing the data set to identify a second latent factor of the content item based on the data set; grouping the first latent factor and the second latent factor into a cluster, and learning the second latent factor of the content item based on the cluster; and program code for training a neural network to learn features by learning to classify a plurality of content items based at least partially on the first or the second feature of the content item. 16. The non-transitory computer-readable medium of claim 15, wherein the program code further comprises: program code for receiving, by the processor, data associated with a query content item; program code for determining, by the processor, latent factors of the query content item; program code for learning, by the processor, features of the query content item; and program code for outputting, by the processor, a recommendation indicating a recommended content item, wherein the recommended content item is similar to the query content item. 17. The non-transitory computer-readable medium of claim 15, wherein the program code further comprises: program code for determining, by the processor, whether the user providing the text query clicked on the content item resulting from the search of the text query; and program code for outputting, by the processor, a recommendation to the user indicating a recommended content item, wherein the recommended content item is similar to the content item clicked on by the user. 18. The non-transitory computer-readable medium of claim 15, wherein obtaining the data set includes removing, by the processor, outlier data from the data set to improve an accuracy with which the neural network classifies the plurality of content items, wherein outlier data includes data in the data set that is above or below a threshold. 19. The non-transitory computer-readable medium of claim 15, wherein the data set is a data matrix and wherein determining the first latent factor or the second latent factor of the content item includes: decomposing the data matrix into a plurality of data matrices that include data associated with the first latent factor or the second latent factor; and deriving the first latent factor or the second latent factor from the data in the plurality of data matrices. 20. The non-transitory computer-readable medium of claim 19, wherein the program code further comprises: program code for grouping, by the processor, the first latent factor and the second latent factor into a plurality of clusters; and program code for learning the first feature or the second feature of the content item based on the plurality of clusters.