Incremental learning on background net to capture changing personal reading preference

This article proposes a novel approach for capturing user´s personal preference of reading by a long-term knowledge background accumulated through incremental learning on user´s favorite articles, to better serve personal article selection. User´s knowledge background is represented as weighted undirected graph called background net that captures the contextual association of words appeared in the articles recommended. With a background net of user constructed, the understanding of a word is personalized to a fuzzy set based on contextual association of the given word to other words involved in the user´s background net. Similarity and acceptance measures are defined to evaluate candidate article through associate reasoning on background net.