사용자 기반 협력 필터링을 위한 유사도 최적화
Optimizing Similarity for User-based Collaborative Filtering
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초록
협력 필터링은 가장 널리 알려진 추천 시스템의 구현 방식들 중의 하나로서, 유사한 사용자들이과거에 선호하였던 항목들을 추천한다. 따라서 유사도 측정은 시스템의 성능을 좌우하는 매우 중요한 요소이다. 본 연구에서는 기존의 단일의 또는 통합된 휴리스틱 유사도 척도의 단점을 해결하기위한 목적으로, 유전 알고리즘을 활용하여 항목 장르별로 사용자 간 최적의 유사도를 산출하였다. 또한 데이터 확장성 문제를 해결하기 위하여 미리 설정한 임계치에 따라 각 장르별 유사도 산출 대상 사용자 수를 제한하였고, 데이터 희소성 문제 해결을 위하여 항목의 평가치 평균을 활용하였다. 성능 실험을 통하여 유전 연산자 확률의 적정값을 구하였고, 예측 성능을 분석한 결과, 제안 방법의성능이 기존보다 우수하고, 특히 희소 데이터 환경에서 더욱 우수함을 확인하였다.
Abstract
Collaborative filtering is one of the most widely known implementation methods of recommender systems, which recommends items that similar users have preferred in the past. Therefore, similarity measurement is a very important factor that determines the performance of the system. In this study, in order to solve the shortcomings of the existing single or integrated heuristic similarity measures, the genetic algorithm was used to calculate the optimal similarity between users per item genre. In addition, in order to solve the data scalability problem, the number of users for calculating similarity for each genre was limited according to a preset threshold, and the average of the ratings of the items was used to solve the data sparsity problem. Through performance experiments, the optimal probabilities of the genetic operators were obtained and the prediction accuracy performance was analyzed. As a result, it was confirmed that the performance of the proposed method was superior to the existing methods, especially in a sparse data environment.
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