Abstract
Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recommendation algorithm has already been used successfully at recommender system. However, with the development of E-commerce, the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods, we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model (IC-Based CF) computes the similarity degree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.
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Foundation item: Supported by the National Basic Research Program of China (973 Program) (2006CB701305, 2007CB310804), the National Natural Science Foundation of China (60743001), Best National Thesis Fund (2005047), and the Natural Science Foundation of Hubei Province (CDB132, 2010j0049)
Biography: WANG Shuliang, Ph. D., Professor, research directions: cloud model, spatial data mining and knowledge discovery.
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Wang, S., Xie, Y. & Fang, M. A collaborative filtering recommendation algorithm based on item and cloud model. Wuhan Univ. J. Nat. Sci. 16, 16–20 (2011). https://doi.org/10.1007/s11859-011-0704-4
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DOI: https://doi.org/10.1007/s11859-011-0704-4