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The cold-start problem is a primary factor causing performance loss in collaborative filtering. In this paper, we examine a fatal flaw of existing similarity measures in the cold-start condition. We propose a novel method, MSRV, using the moment of a random variable to solve the weaknesses of existing similarity measures that contain vector cosine similarity and correlation analysis-based methods. The proposed method is based on a prudent concept; if the expectation of the difference between two random variables is low, they will be similar to each other. We improve memory-based collaborative filtering performance using the moment that is a major statistical parameter. An experiment using various datasets confirms that the proposed method demonstrates significantly improved prediction performance compared to existing measures in full rating experiments.
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on (Volume:3 )
Date of Conference: 21-22 Nov. 2009