Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Optimal Similarity Calculation using Genetic Algorithms in Collaborative Filtering

협력 필터링에서 유전자 알고리즘을 활용한 최적 유사도 산출

  • Soojung Lee (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2024.07.08
  • Accepted : 2024.10.04
  • Published : 2024.10.31
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Abstract

A collaborative filtering-based recommender system is a method that gives priority to items preferred by similar neighbors when providing recommended items for the current user. The similarity measure is very important for the performance of the system. In this study, a genetic algorithm was used to calculate the similarity value between users that results in optimal performance. In particular, the genetic algorithm was run separately for each rated item feature to improve prediction accuracy performance. Through performance experiments, the optimal probabilities of the genetic algorithm operators were obtained, and as a result of experiments using two types of public datasets, it was confirmed that the prediction performance of the proposed method was superior to that of existing methods, especially in a sparse data environment. The results of this study can improve the accuracy of personalized recommendations and be effectively applied in real-world applications with large-scale user and item data.

협력 필터링 기반의 추천 시스템은 현 사용자를 위한 추천 항목들을 제공함에 있어서 유사한 인접 이웃들이 선호한 항목들을 우선적으로 고려하는 방식이다. 시스템의 성능을 위하여 유사도 척도는 매우 중요한데, 본 연구에서는 유전자 알고리즘을 활용하여 최적 성능을 가져오는 사용자 간 유사도 값을 산출하였으며, 특히 평가 항목 특성별로 유전자 알고리즘을 별도 실행하여 예측 정확도 성능을 높이고자 하였다. 성능 실험을 통하여 유전자 알고리즘의 연산 확률 적정값을 구하였고, 두 종류의 공개 데이터셋을 활용한 실험 결과로서 제안 방법의 예측 성능이 기존 방법들보다 우수하고, 특히 희소 데이터 환경에서 더욱 우수함을 확인하였다. 본 연구 결과는 개인화된 추천의 정확성을 개선하고, 대규모 사용자 및 항목 데이터가 존재하는 실세계 애플리케이션에서 유용하게 활용될 수 있다.

Keywords

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