Improvement of Information Filtering by Independent Components Selection
Takeru Yokoi, Hidekazu Yanagimoto, Sigeru Omatu · IEEJ Transactions on Electronics Information and Systems · 2006
We propose an improvement of an information filtering system with independent components selection. The independent components are obtained by Independent Component Analysis and considered as topics. It is effective for improving accuracy of information filtering to select some similar topics by focusing on these meaning. To achieve this, we select the topics by Maximum Distance Algorithm with Jensen-Shannon divergence. In addition, document vectors are represented by the selected topics. We create a user profile from transformed data with a relevance feedback. Finally, we recommend documents by the user profile and evaluate the accuracy by imputation precision. We carry out an evaluation experiment to confirm availability of the proposed method and also consider the meaning of components in this experiment.