Information filtering using SVD and ICA

Abstract

We propose an information filtering system for documents by a user profile using latent semantics obtained by singular value decomposition (SVD) and independent component analysis (ICA). In information filtering systems, it is useful to analyze the latent semantics of documents. ICA is one method to analyze the latent semantics. We assume that topics are independent of each other. Hence, when ICA is applied to documents, we obtain the topics included in the documents. By using SVD remove noises before applying ICA, we can improve the accuracy of topic extraction. By representation of the documents with those topics, we improve recommendations by the user profile. In addition, we construct a user profile with a genetic algorithm (GA) and evaluate it by 11-point average precision. We carried out an experiment using a test collection to confirm the advantages of the proposed method.