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Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(5), pp.47-53
  • DOI : 10.9708/jksci.2021.26.05.047
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 21, 2021
  • Accepted : May 19, 2021
  • Published : May 31, 2021

Soojung Lee 1

1경인교육대학교

Accredited

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.

Citation status

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