메모리 기반 협력필터링을 위한 평가 등급 범위를 이용한 유사도 척도
A Similarity Measure Using Rating Ranges for Memory-based Collaborative Filtering
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초록
협력 필터링은 사용자가 선호했던 항목들의 기록을 토대로 항목을 추천하는 방법으로서 상업 사이트에서 매우 널리 사용되어 왔다. 이 방식의 기본 개념은 유사한 사용자들을 찾아서 그들의 평가등급을 통합하여 새로운 항목 추천에 이용하는 것이다. 따라서 유사도의 정확한 측정은 추천 성능에 매우 중요한 일이다. 본 논문에서는 사용자가 과거에 부여했던 평가등급들을 기준으로 하여 상대적으로 각 평가치를 다루는 새로운 유사도 공식을 제안한다. 광범위한 실험을 통해 제안된 공식이 기존 공식들보다 더 신뢰할 수 있음을 밝혔는데, 이는 극단적인 유사도값의 발생이 현저히 감소하였고, 유사도가 큰 이웃들만을 참조하였을 때 성능이 개선되었기 때문이다. 특히 실험 결과, 제안 공식은 평가 범위가 큰 데이터셋에 대해 기존 공식들보다 우수한 성능을 나타냈다.
Abstract
Collaborative filtering has been most widely used in commercial sites to recommend items based on the history of user preferences for items. The basic idea behind this method is to find similar users whose ratings for items are incorporated to make recommendations for new items. Hence, similarity calculation is most critical in recommendation performance. This paper presents a new similarity measure that takes each rating of a user relatively to his own ratings. Extensive experiments revealed that the proposed measure is more reliable than the classic measures in that it significantly decreases generation of extreme similarity values and its performance improves when consulting neighbors with high similarites only. In particular, the results show that the proposed measure is superior to the classic ones for datasets with large rating scales.
참고문헌 (14)
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