7. The method of claim 1 , wherein generating the model of user interests further comprises: mapping a set of topics into a joint embedding space, wherein each of the set of topics is assigned a location within the joint embedding space; mapping a set of content items into the joint embedding space, wherein each of the set of content items is associated with one or more topics in the set of topics; learning one or more mappings for the set of topics and the set of content items in the joint embedding space to create a learned joint embedding space; projecting the plurality of topics into the learned joint embedding space; and determining associations between the plurality of topics and the set of content items.
7. The method of claim 1 , wherein generating the model of user interests further comprises: mapping a set of topics into a joint embedding space, wherein each of the set of topics is assigned a location within the joint embedding space; mapping a set of content items into the joint embedding space, wherein each of the set of content items is associated with one or more topics in the set of topics; learning one or more mappings for the set of topics and the set of content items in the joint embedding space to create a learned joint embedding space; projecting the plurality of topics into the learned joint embedding space; and determining associations between the plurality of topics and the set of content items. 8. The method of claim 7 , wherein learning the one or more mappings for the set of topics and the set of content items further comprising training mapping functions associated with each of the set of mapped topics and the set of mapped content items such that a loss function is minimized.