Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDM.2008.68
Title: Learning the latent semantic space for ranking in text retrieval
Authors: Yan, J.
Yan, S. 
Liu, N.
Chen, Z.
Issue Date: 2008
Citation: Yan, 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
Abstract: Subspace 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.
Source Title: Proceedings - IEEE International Conference on Data Mining, ICDM
URI: http://scholarbank.nus.edu.sg/handle/10635/83895
ISBN: 9780769535029
ISSN: 15504786
DOI: 10.1109/ICDM.2008.68
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

27
checked on Jun 8, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.