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A New Similarity Measure based on Separation of Common Ratings for Collaborative Filtering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(11), pp.149-156
  • DOI : 10.9708/jksci.2021.26.11.149
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 9, 2021
  • Accepted : November 12, 2021
  • Published : November 30, 2021

Soojung Lee 1

1경인교육대학교

Accredited

ABSTRACT

Among various implementation techniques of recommender systems, collaborative filtering selects nearest neighbors with high similarity based on past rating history, recommends products preferred by them, and has been successfully utilized by many commercial sites. Accurate estimation of similarity is an important factor that determines performance of the system. Various similarity measures have been developed, which are mostly based on integrating traditional similarity measures and several indices already developed. This study suggests a similarity measure of a novel approach. It separates the common rating area between two users by the magnitude of ratings, estimates similarity for each subarea, and integrates them with weights. This enables identifying similar subareas and reflecting it onto a final similarity value. Performance evaluation using two open datasets is conducted, resulting in that the proposed outperforms the previous one in terms of prediction accuracy, rank accuracy, and mean average precision especially with the dense dataset. The proposed similarity measure is expected to be utilized in various commercial systems for recommending products more suited to user preference.

Citation status

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