In one embodiment, a method may include receiving a first content item. A first embedding of the first content item may be determined and may corresponds to a first point in an embedding space. The embedding space may include a plurality of second points corresponding to a plurality of second embeddings of second content items. The embeddings are determined using a deep-learning model. The points are located in one or more clusters in the embedding space, which are each associated with a class of content items. Locations of points within clusters may be based on one or more attributes of the respective corresponding content items. Second content items that are similar to the first content item may be identified based on the locations of the first point and the second points and on particular clusters that the second points corresponding to the identified second content items are located in.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by one or more computing devices, a first content item; determining, by one or more computing devices, a first embedding of the first content item, wherein: the first embedding corresponds to a first point in an embedding space, the embedding space comprises a plurality of second points corresponding to a plurality of second embeddings of second content items, the first and second embeddings are determined using a deep-learning model, the first and second points are located in one or more clusters in the embedding space, each of the clusters is associated with a class of content items, and the first and second points are further located within the clusters based on one or more attributes of the first and second content items; and identifying, by one or more computing devices, one or more of the second content items that are similar to the first content item based on: the location of the first point, one or more particular clusters that the one or more second points corresponding to the one or more second content items are located in, and the locations of the one or more second points corresponding to the one or more second content items within the particular clusters. 2. The method of claim 1, wherein the deep-learning model is trained using a loss function that reduces overlap between points located in the one or more clusters. 3. The method of claim 1, wherein the one or more attributes of the first and second content items are latent input variables of the deep-learning model. 4. The method of claim 1, wherein the deep-learning model is a neural network. 5. The method of claim 1, wherein the first content item and the second content items are each visual content, and wherein the one or more attributes of the first and second content items comprise one or more of color, pose, lighting conditions, scene geometry, material, texture, size, and granularity. 6. The method of claim 1, wherein the first content item is a search query received at a client system of a user. 7. The method of claim 6, further comprising sending, to the client system, the one or more identified second content items for display to the user. 8. The method of claim 1, wherein a type of content of the first content item comprises one or more of text content, image content, audio content, and video content. 9. The method of claim 8, further comprising determining the type of content of the first content item, and wherein identifying one or more of the second content items is further based on the type of content of the first content item. 10. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: receive a first content item; determine a first embedding of the first content item, wherein: the first embedding corresponds to a first point in an embedding space, the embedding space comprises a plurality of second points corresponding to a plurality of second embeddings of second content items, the first and second embeddings are determined using a deep-learning model, the first and second points are located in one or more clusters in the embedding space, each of the clusters is associated with a class of content items, and the first and second points are further located within the clusters based on one or more attributes of the first and second content items; and identify one or more of the second content items that are similar to the first content item based on: the location of the first point, one or more particular clusters that the one or more second points corresponding to the one or more second content items are located in, and the locations of the one or more second points corresponding to the one or more second content items within the particular clusters. 11. The media of claim 10, wherein the deep-learning model is trained using a loss function that reduces overlap between points located in the one or more clusters. 12. The media of claim 10, wherein the one or more attributes of the first and second content items are latent input variables of the deep-learning model. 13. The media of claim 10, wherein the first content item and the second content items are each visual content, and wherein the one or more attributes of the first and second content items comprise one or more of color, pose, lighting conditions, scene geometry, material, texture, size, and granularity. 14. The media of claim 10, wherein the first content item is a search query received at a client system of a user. 15. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive a first content item; determine a first embedding of the first content item, wherein: the first embedding corresponds to a first point in an embedding space, the embedding space comprises a plurality of second points corresponding to a plurality of second embeddings of second content items, the first and second embeddings are determined using a deep-learning model, the first and second points are located in one or more clusters in the embedding space, each of the clusters is associated with a class of content items, and the first and second points are further located within the clusters based on one or more attributes of the first and second content items; and identify one or more of the second content items that are similar to the first content item based on: the location of the first point, one or more particular clusters that the one or more second points corresponding to the one or more second content items are located in, and the locations of the one or more second points corresponding to the one or more second content items within the particular clusters. 16. The system of claim 15, wherein the deep-learning model is trained using a loss function that reduces overlap between points located in the one or more clusters. 17. The system of claim 15, wherein the one or more attributes of the first and second content items are latent input variables of the deep-learning model. 18. The system of claim 15, wherein the first content item and the second content items are each visual content, and wherein the one or more attributes of the first and second content items comprise one or more of color, pose, lighting conditions, scene geometry, material, texture, size, and granularity. 19. The system of claim 15, wherein the first content item is a search query received at a client system of a user.