Department of Computer Science and Technology, Jilin University, Changchun 130012, China

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Abstract—A composite collaborative filtering algorithm for personalized recommend will be presented to solve the original Collaborative Filtering algorithm problem including“None of User Starting ”and “Data Sparsity”, and the Spearman rank correlation coefficient will be used as a main correlation coefficient. Top-M commended is going to be used to get the final results in this paper. At last, we will validate that this algorithm is superior to the algorithm of collaborative filtering based on user and the algorithm of collaborative filtering based on item.

Index Terms—composite collaborative filtering algorithm; none of user starting; data sparsity; top-M.

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Cite: Liang Hu, Wenbo Wang, Feng Wang, Xiaolu Zhang, Kuo Zhao, "The Design and Implementation of Composite Collaborative Filtering Algorithm for Personalized Recommendation," Journal of Software vol. 7, no. 9, pp. 2040-2045, 2012.