Reducing Noise Using Degree of Scattering in Collaborative Filtering System 


Vol. 14,  No. 7, pp. 549-558, Dec.  2007
10.3745/KIPSTB.2007.14.7.549


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  Abstract

Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.

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[IEEE Style]

S. J. Ko, "Reducing Noise Using Degree of Scattering in Collaborative Filtering System," The KIPS Transactions:PartB , vol. 14, no. 7, pp. 549-558, 2007. DOI: 10.3745/KIPSTB.2007.14.7.549.

[ACM Style]

Su Jeong Ko. 2007. Reducing Noise Using Degree of Scattering in Collaborative Filtering System. The KIPS Transactions:PartB , 14, 7, (2007), 549-558. DOI: 10.3745/KIPSTB.2007.14.7.549.

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