• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

The Effect of Data Sparsity on Prediction Accuracy in Recommender System


Sun-Ok Kim, Seok-Jun Lee, Journal of Internet Computing and Services, Vol. 8, No. 6, pp. 95-102, Dec. 2007
Full Text:
Keywords: Recommender System, Sparsity, Mean Absolute Error, Collaborative Filtering

Abstract

Recommender System based on the Collaborative Filtering has a problem of trust of the prediction accuracy because of its problem of sparsity. If the sparsity of a preference value is large, it causes a problem on a process of a choice of neighbors and also lowers the prediction accuracy. In this article, a change of MAE based on the sparsity is studied, groups are classified by sparsity and then, the significant difference among MAEs of classified groups is analyzed. To improve the accuracy of prediction among groups by the problem of sparsity, We studied the improvement of an accurate prediction for recommending system through reducing sparsity by sorting sparsity items, and replacing the average preference among them that has a lot of respondents with the preference evaluation value.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[APA Style]
Sun-Ok Kim and Seok-Jun Lee (2007). The Effect of Data Sparsity on Prediction Accuracy in Recommender System. Journal of Internet Computing and Services, 8(6), 95-102.

[IEEE Style]
S. Kim and S. Lee, "The Effect of Data Sparsity on Prediction Accuracy in Recommender System," Journal of Internet Computing and Services, vol. 8, no. 6, pp. 95-102, 2007.

[ACM Style]
Sun-Ok Kim and Seok-Jun Lee. 2007. The Effect of Data Sparsity on Prediction Accuracy in Recommender System. Journal of Internet Computing and Services, 8, 6, (2007), 95-102.