Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDM.2008.68
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dc.titleLearning the latent semantic space for ranking in text retrieval
dc.contributor.authorYan, J.
dc.contributor.authorYan, S.
dc.contributor.authorLiu, N.
dc.contributor.authorChen, Z.
dc.date.accessioned2014-10-07T04:46:25Z
dc.date.available2014-10-07T04:46:25Z
dc.date.issued2008
dc.identifier.citationYan, J., Yan, S., Liu, N., Chen, Z. (2008). Learning the latent semantic space for ranking in text retrieval. Proceedings - IEEE International Conference on Data Mining, ICDM : 1115-1120. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDM.2008.68
dc.identifier.isbn9780769535029
dc.identifier.issn15504786
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83895
dc.description.abstractSubspace learning techniques for text analysis, such as Latent Semantic Indexing (LSI), have been widely studied in the past decade. However, to our best knowledge, no previous study has leveraged the rank information for subspace learning in ranking tasks. In this paper, we propose a novel algorithm, called Learning Latent Semantics for Ranking (LLSR), to seek the optimal Latent Semantic Space tailored to the ranking tasks. We first present a dual explanation for the classical Latent Semantic Indexing (LSI) algorithm, namely learning the so-called Latent Semantic Space (LSS) to encode the data information. Then, to handle the increasing amount of training data for the practical ranking tasks, we propose a novel objective function to derive the optimal LSS for ranking. Experimental results on two SMART sub-collections and a TREC dataset show that LLSR effectively improves the ranking performance compared with the classical LSI algorithm and ranking without subspace learning. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDM.2008.68
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICDM.2008.68
dc.description.sourcetitleProceedings - IEEE International Conference on Data Mining, ICDM
dc.description.page1115-1120
dc.identifier.isiut000264173600140
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