Nearest-Neighbor Collaborative Filtering Using Dimensionality Reduction by Non-negative Matrix Factorization 


Vol. 13,  No. 6, pp. 625-632, Dec.  2006
10.3745/KIPSTB.2006.13.6.625


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  Abstract

Collaborative filtering is a technology that aims at learning predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn´t rate by a new user based on the values that the nearest neighbors rated items.

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[IEEE Style]

S. J. Ko, "Nearest-Neighbor Collaborative Filtering Using Dimensionality Reduction by Non-negative Matrix Factorization," The KIPS Transactions:PartB , vol. 13, no. 6, pp. 625-632, 2006. DOI: 10.3745/KIPSTB.2006.13.6.625.

[ACM Style]

Su Jeong Ko. 2006. Nearest-Neighbor Collaborative Filtering Using Dimensionality Reduction by Non-negative Matrix Factorization. The KIPS Transactions:PartB , 13, 6, (2006), 625-632. DOI: 10.3745/KIPSTB.2006.13.6.625.

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