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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.
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on (Volume:4 )
Date of Conference: 10-13 July 2011