Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A New Kernelized Approach to Recommender System

커널 함수를 도입한 새로운 추천 시스템

  • Received : 2011.06.23
  • Accepted : 2011.10.10
  • Published : 2011.10.25
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Abstract

In this paper, a new kernelized approach for use in a recommender system (RS) is proposed. Using a machine learning technique, the proposed method predicts the user's preferences for unknown items and recommends items which are likely to be preferred by the user. Since the ratings of the users are generally inconsistent and noisy, a robust binary classifier called a dual margin Lagrangian support vector machine (DMLSVM) is employed to suppress the noise. The proposed method is applied to MovieLens databases, and its effectiveness is demonstrated via simulations.

본 논문에서는 커널 함수를 이용한 기법을 통한 추천 시스템을 제안한다. 제안된 추천 시스템은 기계 학습 기법을 이용하여 새로운 아이템에 대한 사용자의 선호도를 예측하고 예측된 결과를 바탕으로 사용자가 선호할만한 아이템들을 추천한다. 일반적으로 사용자의 평가 정보는 잡음이 포함되어 있고 일관성이 적으므로 잡음에 영향을 적게 받는 이원 분류기인 이중 마진 Lagrangian support vector machine (DMLSVM) 을 사용한다. 제안된 기법은 MovieLens 데이터베이스에 적용하였다. 또한 시뮬레이션을 통해 제안된 방법의 우수성을 확인하였다.

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References

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