협력필터링에서 자카드 계수에 기반한 서로 다른 유사도 척도의 적용
Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering
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초록
희소한 평가 데이터는 사용자들 간의 신뢰할만한 유사도 산출을 저해하기 때문에 추천 시스템을위한 메모리 기반의 협력 필터링 기법의 성능을 저하시킨다. 기존 연구의 많은 결과물은 이 데이터희소성 문제를 해결하기 위해 개발되었으며, 가장 단순하고 대표적인 업적은 자카드 계수를 활용하는 방법들이다. 이 계수는 두 사용자의 공통 평가 항목수를 반영하며, 그들 간의 유사도를 보다 정확하게 계산하기 위해 전통적인 유사도 척도와 통합된다. 그러나, 그러한 통합은 데이터 희소성의 정도를 고려하지 않은 매우 단순한 방법이다. 본 연구는 두 사용자의 자카드 계수값에 의거하여 다른유사도 척도를 적용하는 새로운 아이디어를 제시한다. 제안 방법에서 사용하는 파라미터의 최적값을 구하기 위하여 성능 실험을 진행하였고, 다른 관련된 방법들과 비교 평가하였다. 결과로서, 제안방법은 예측 정확도와 추천 정확도에 있어서 가장 우수하거나 대등한 성능을 보였다.
Abstract
Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.
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