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Information filtering using latent semantics
Author(s) -
Yokoi Takeru,
Yanagimoto Hidekazu,
Omatu Sigeru
Publication year - 2008
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20564
Subject(s) - singular value decomposition , dimension (graph theory) , computer science , semantics (computer science) , independent component analysis , latent semantic analysis , construct (python library) , vector space model , artificial intelligence , pattern recognition (psychology) , data mining , mathematics , pure mathematics , programming language
We propose an information filtering system using latent semantics obtained by Singular Value Decomposition (SVD) and Independent Component Analysis (ICA). Document vectors usually have too many elements. Thus, we are obliged to spend much time applying the ICA for the document vectors. To solve this problem, the present method combines the SVD which is often used for decreasing dimension and the ICA. Before applying the ICA, we represent documents with singular vectors obtained by the SVD. We measure processing times to carry out the ICA without the SVD and the proposed method for comparison of these methods. In addition, we construct a user profile in space consisting of latent semantics obtained by the present method, and discuss accuracy of recommendation. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 165(2): 53–59, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20564