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Notice of Retraction
Enhancement of collaborative filtering performance under data scarcity environment

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3 Author(s)
Seok Jun Lee ; Dept. of Manage. Inf. Syst., Sangji Unviersity, Wonju, South Korea ; Hee Choon Lee ; Sun Ok Kim

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

This study devotes to improve the prediction accuracy of prediction algorithms in recommender systems which one is collaborative filtering algorithm to estimate user's preference to items transacted on the web. From the our experiment, data scarcity problem is critical factor for decreasing prediction accuracy so the method for reducing data scarcity is meaningful way to increase prediction accuracy and also techniques for improving prediction accuracy like as the significant weight must be applied to the prediction process. This study proposes substitution methods like as the means of modes of users and items are effective and economical ways to reduce data scarcity better than other complicate substitution methods and can get even more accurate result than original result.

Published in:

New Trends in Information Science and Service Science (NISS), 2010 4th International Conference on

Date of Conference:

11-13 May 2010