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Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(12), pp.219-226
  • DOI : 10.9708/jksci.2018.23.12.219
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 15, 2018
  • Accepted : November 8, 2018
  • Published : December 31, 2018

Soojung Lee 1

1경인교육대학교

Accredited

ABSTRACT

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

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