Bias-Based Predictor to Improve the Recommendation Performance of the Rating Frequency Weight-based Baseline Predictor 


Vol. 44,  No. 5, pp. 486-495, May  2017


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  Abstract

Collaborative Filtering is limited because of the cost that is required to perform the recommendation (such as the time complexity and space complexity). The RFWBP (Rating Frequency Weight-based Baseline Predictor) that approximates the precision of the existing methods is one of the efficiency methods to reduce the cost. But, the following issues need to be considered regarding the RFWBP: 1) It does not reduce the error because the RFWBP does not learn for the recommendation, and 2) it recommends all of the items because there is no condition for an appropriate recommendation list when only the RFWBP is used for the achievement of efficiency. In this paper, the BBP (Bias-Based Predictor) is proposed to solve these problems. The BBP reduces the error range, and it determines some of the cases to make an appropriate recommendation list, thereby forging a recommendation list for each case.


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  Cite this article

[IEEE Style]

T. Hwang and S. K. Kim, "Bias-Based Predictor to Improve the Recommendation Performance of the Rating Frequency Weight-based Baseline Predictor," Journal of KIISE, JOK, vol. 44, no. 5, pp. 486-495, 2017. DOI: .


[ACM Style]

Tae-Gyu Hwang and Sung Kwon Kim. 2017. Bias-Based Predictor to Improve the Recommendation Performance of the Rating Frequency Weight-based Baseline Predictor. Journal of KIISE, JOK, 44, 5, (2017), 486-495. DOI: .


[KCI Style]

황태규, 김성권, "평점 빈도 가중치 기반 기준선 예측기의 추천 성능 향상을 위한 편향 기반 추천기," 한국정보과학회 논문지, 제44권, 제5호, 486~495쪽, 2017. DOI: .


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