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Integration of Similarity Values Reflecting Rating Time for Collaborative Filtering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(1), pp.83-89
  • DOI : 10.9708/jksci.2022.27.01.083
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 13, 2021
  • Accepted : January 5, 2022
  • Published : January 28, 2022

Soojung Lee 1

1경인교육대학교

Accredited

ABSTRACT

As a representative technique of recommender systems, collaborative filtering has been successfully in service through many commercial and academic systems. This technique recommends items highly rated by similar neighbor users, based on similarity of ratings on common items rated by two users. Recently research on time-aware recommender systems has been conducted, which attempts to improve system performance by reflecting user rating time of items. However, the decay rate uniform to past ratings has a risk of lowering the rating prediction performance of the system. This study proposes a rating time-aware similarity measure between users, which is a novel approach different from previous ones. The proposed approach considers changes of similarity value over time, not item rating time. In order to evaluate performance of the proposed method, experiments using various parameter values and types of time change functions are conducted, resulting in improving prediction accuracy of existing traditional similarity measures significantly.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.