Abstract
Latent semantic indexing (LSI) is a rank-reduced vector space model and has demonstrated an improved retrieval performance over traditional lexical searching methods. By applying the singular value decomposition (SVD) to the original term by document space, LSI transforms individual terms into the statistically derived conceptual indices and is capable of retrieving information based on the semantic content. Recently, an updated LSI model, referred to as RSVD- LSI, has been proposed [5,6] for effective information retrieval. It updates LSI based on user feedback and can be formulated By a modified Riemannian SVD for a low-rank matrix. In this paper, an new efficient implementation of RSVD-LSI is discribed and the applications and performance analysis of RSVD-LSI on dynamic document collections are discussed. The effectiveness of RSVD-LSI as a conceptual information retrieval technique is demonstrated by experiments on some document collections.
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