Learning latent semantic space for ranking
First Claim
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1. A computer-implemented method of subspace learning for ranking, the method comprising:
- learning from a plurality of labeled queries;
applying the learning from the labeled queries for ranking unranked documents;
obtaining, via the applying, a learned latent semantic space (LSS) for ranking unranked documents;
providing ranking for unranked documents in the learned LSS;
reporting the learned LSS including the unranked documents based at least in part on the ranking.
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Abstract
A tool facilitating learning latent semantics for ranking (LLSR) tailored to the ranking task via leveraging relevance information of query-document pairs to learn a tailored latent semantic space such that other documents are better ranked for the queries in the subspace. The tool applying a learning latent semantics for ranking algorithm integrating LLSR, thereby enabling learning an optimal latent semantic space (LSS) for ranking by utilizing relevance information in the training process of subspace learning. The tool enabling an optimization of the LSS as a closed form solution and facilitating reporting the learned LSS.
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Citations
20 Claims
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1. A computer-implemented method of subspace learning for ranking, the method comprising:
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learning from a plurality of labeled queries; applying the learning from the labeled queries for ranking unranked documents; obtaining, via the applying, a learned latent semantic space (LSS) for ranking unranked documents; providing ranking for unranked documents in the learned LSS; reporting the learned LSS including the unranked documents based at least in part on the ranking. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A memory having computer-executable instructions embodied thereon, the computer-executable instructions to configure a computer to perform acts comprising:
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subspace learning for ranking; and returning a search based at least in part on the subspace learning for ranking. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A method comprising:
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receiving as a training set, a set of ranked query-document pairs; summarizing global text information from documents in the training set by computing a first matrix; summarizing rank information from the training data by computing a second matrix; applying the first matrix to the second matrix to smooth the second matrix to obtain a third matrix; computing eigenvectors of the third matrix as column vectors of a subspace; and reporting the subspace as a latent semantic space for ranking. - View Dependent Claims (18, 19, 20)
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Specification