Collaborative Filtering Recommendation Model Based on k-means Clustering

  • Nadia Fadhil AL-Bakri Department of Computer Science, AL Nahrain University, Baghdad-Iraq.
  • Soukaena Hassan Hashim Department of Computer Science, University of Technology, Baghdad-Iraq.
Keywords: K-means, recommender system, clustering, movies, Collaborative filtering


In this age of information load, it becomes a herculean task for user to get the relevant things from vast number of information. This huge number of data demand specially designed Recommender system that can plays an important role in suggesting relevant information preferred by the users. From this point, this paper presents a modest approach to enhance prediction in MovieLens dataset with high scalability by applying user-based collaborative filtering methods on clustered data. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting K-means clustering and correlation coefficient similarity measures against MovieLens datasets lead to an increase in the scalability of recommender system.

How to Cite
AL-Bakri, N. F., & Hashim, S. H. (2019). Collaborative Filtering Recommendation Model Based on k-means Clustering. Al-Nahrain Journal of Science, 22(1), 74-79. Retrieved from

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