Clickthrough-based latent semantic model

A system for ranking documents, comprising:

• a processor that is adapted to execute stored instructions;
• and
• a system memory, wherein the system memory comprises code configured to:
• identify a plurality of query-title pairs based on clickthrough data for a plurality of documents;
• build a latent semantic model based on the plurality of query-title pairs by building a bilingual topic model by learning a semantic representation of a query based on a likelihood that the query is a semantics-based translation of each of the plurality of documents;
• and
• rank the plurality of documents for a search based on the latent semantic model.

One or more non-transitory, computer-readable storage media for storing computer-readable instructions, the computer-readable instructions providing a document ranking system when executed by one or more processing devices, the computer-readable instructions comprising code configured to:

• identify a plurality of query-title pairs based on clickthrough data for a plurality of documents;
• build a latent semantic model by using the plurality of query-title pairs to learn a projection matrix, wherein the latent semantic model is built by building a bilingual topic model to learn a semantic representation of a query based on a likelihood that the query is a semantics-based translation of each of the plurality of documents;
• map vector representations of a query and a title of each of the plurality of documents to a semantic space using the projection matrix;
• and
• rank the plurality of documents for a search based on a distance between the vector representations of the query and the title of each of the plurality of documents within the semantic space.