추천 시스템은 개인화 서비스의 한 종류로서 사용자의 요구 사항에 적합한 상품이나 서비스를 추천한다. 이러한 추천 기법 중 하나인 협력적 필터링은 우수한 성능(정확도와 계산 속도 측면)으로 인해 많이 사용되지만, 상황에 따라 적합한 추천을 하지 못하기도 한다. 이를 해결하기 위해 새로운 복합 추천 시스템을 제안한다. 먼저 콘텐츠 기반의 추천 시스템을 구축하고, 이 시스템이 예측하는 평점들을 다양한 신뢰도 척도를 사용하여 양질의 일부분만 선택한다. 선택된 평점들을 사용자가 실제로 매긴 평점 데이터에 더한 후, 이 모두를 협력적 필터링의 입력으로 사용하여 평점을 예측한다. 이렇게 제안한 시스템은 기존의 여러 협력적 필터링 방법의 추천 정확도를 추가적으로 상승시켰으며, 더욱 안전하게 콘텐츠 정보를 통합할 수 있다.
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