Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Similarity Measure based on Utilization of Rating Distributions for Data Sparsity Problem in Collaborative Filtering

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2020.10.26
  • Accepted : 2020.12.10
  • Published : 2020.12.31
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Abstract

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users' ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.

메모리 기반의 협력 필터링은 추천 시스템의 대표적인 타입이지만 데이터 희소성이라는 본질적인 문제를 갖고 있다. 이 문제를 해결하기 위해 많은 연구 업적들이 이루어졌으나, 보다 체계적인 접근 방법은 여전히 요구된다. 본 연구는 사용자 간의 유사도를 산출하기 위하여 항목들에 대한 사용자 평가치 분포를 활용한다. 따라서 제안 방법은 사용자의 모든 평가치를 이용하므로, 공통 항목에 대한 평가치만을 이용하는 기존 방법들과 대비된다. 더욱이, 각 항목에 대한 다른 사용자들의 평가치들을 유사도 계산에 반영함으로써 항목 평가치의 광역적인 관점을 취한다. 제안 방법의 성능은 실험을 통하여 평가하였고, 연관된 다른 방법들과 비교하였다. 그 결과, 제안 방법은 예측과 순위 정확도 측면에서 우수한 성능을 보였다. 이러한 예측 정확도의 향상은 전통적인 유사도 척도에 비해 최근의 방법으로 달성한 것보다 최고 2.6배 더 높다.

Keywords

References

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