Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Bias-Based Predictor to Improve the Recommendation Performance of the Rating Frequency Weight-based Baseline Predictor

평점 빈도 가중치 기반 기준선 예측기의 추천 성능 향상을 위한 편향 기반 추천기

  • 황태규 (중앙대학교 컴퓨터공학과) ;
  • 김성권 (중앙대학교 컴퓨터공학과)
  • Received : 2016.08.16
  • Accepted : 2017.03.01
  • Published : 2017.05.15
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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.

협업 필터링(CF, Collaborative Filtering)은 추천을 수행하기 위해 필요한 비용(시간/공간 복잡도 등)이 현실 데이터에 적용하기에는 한계가 있다. 평점 빈도 가중치 기반의 Baseline Predictor(RFWBP, Rating Frequency Weight-based Baseline Predictor)는 정확도가 기존의 방법과 근사하며, 비용을 크게 줄일 수 있는 효율적인 방법 중 하나이다. 그러나 효율성을 고려해 RFWBP만 사용할 경우, 1)학습을 수행하지 않기 때문에 발생되는 오차를 감소시킬 수 없고, 2)적합한 추천 목록을 작성하기 위한 조건이 없기 때문에 모두 추천했다. 본 논문은, 제시된 문제를 해결하기 위한 BBP(Bias-Based Predictor)를 제안한다. BBP는 Bias를 보정하여 오차의 범위를 감소시킴으로써 1)을 해결했고, 선호에 적합한 추천 목록 작성을 위한 몇 가지 Case를 정하고, 추천 목록을 구성함으로써 2)를 해결하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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