14. A method as recited in claim 10 , further comprising generating a recommendation using the generative model, the generating the recommendation comprising: obtaining a topic distribution of a reference document of a first language; obtaining a topic distributions of a plurality of documents of a second language; comparing the topic distributions between the reference document of the first language and the plurality of documents of the second language to identify topics of the documents; and recommending at least a first document of the second language that is related to the reference document based on its identified topics.
10. A method as recited in claim 8 , further comprising inferring a plurality of universal topics from the concept-unit, each of the universal topics of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages, and wherein the inferring the universal topic distribution comprises performing a latent Dirichlet allocation analysis while constraining the set of documents within the concept-unit to share a common topic distribution.