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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.21 no.1, 2015년, pp.119 - 142
이오준 (중앙대학교 컴퓨터공학과) , 유은순 (단국대학교 미디어콘텐츠연구원)
With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences an...
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
CBCF의 문제점은 무엇인가? | CBCF는 적용범위 감소 문제와 성능 불안정 문제에도 불구하고, CF의 확장성 문제와 희박성의 문제를 개선하는데 유용한 방법이다. 때문에 위의 두 가지 문제를 해결하기 위한 다양한 방법들이 제안되고 있다. | |
모델 기반 협업 필터링은 무엇인가? | 이를 위해 다양한 기법들이 제안되었는데, 비교적 도메인의 제약이 적은 협업 필터링이 널리 사용되고 있다. 협업 필터링의 한 종류인 모델 기반 협업 필터링은 기계학습이나 데이터 마이닝 모델을 협업 필터링에 접목한 방법이다. 이는 희박성 문제와 확장성 문제 등의 협업 필터링의 근본적인 한계를 개선하지만, 모델 생성 비용이 높고 성능/확장성 트레이드오프가 발생한다는 한계점을 갖는다. | |
모델 기반 CF의 장점은 무엇인가? | 이 중, 모델 기반 CF는 베이지안(Bayesian) 모델이나 군집화 모델, 의존성 네트워크(dependency network) 등의 모델을 사용해서 CF의 단점을 보완한 방법이다. 이는 희박성(sparsity) 문제와 확장성 문제 등을 개선하며, 예측 성능을 높일 수 있다. 하지만, 모델 생성 비용이 크고(expensive model-building) 성능과 확장성 간의 트레이드오프(trade-off)가 발생한다. |
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