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Hybrid Movie Recommendation System Using Clustering Technique

클러스터링 기법을 이용한 하이브리드 영화 추천 시스템

  • Sophort Siet (Dept. of Software Convergence, Soonchunhyang University) ;
  • Sony Peng (Dept. of Software Convergence, Soonchunhyang University) ;
  • Yixuan Yang (Dept. of Software Convergence, Soonchunhyang University) ;
  • Sadriddinov Ilkhomjon (Dept. of Software Convergence, Soonchunhyang University) ;
  • DaeYoung Kim (Dept. of Software Convergence, Soonchunhyang University) ;
  • Doo-Soon Park (Dept. of Software Convergence, Soonchunhyang University)
  • 싯소포호트 (순천향대학교 소프트웨어융합학과) ;
  • 펭소니 (순천향대학교 소프트웨어융합학과) ;
  • 양예선 (순천향대학교 소프트웨어융합학과) ;
  • 일홈존 (순천향대학교 소프트웨어융합학과) ;
  • 김대영 (순천향대학교 소프트웨어융합학과) ;
  • 박두순 (순천향대학교 소프트웨어융합학과)
  • Published : 2023.05.18

Abstract

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

Keywords