본문 바로가기
  • Home

Time-aware Collaborative Filtering with User- and Item-based Similarity Integration

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(9), pp.149-155
  • DOI : 10.9708/jksci.2022.27.09.149
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 2, 2022
  • Accepted : August 29, 2022
  • Published : September 30, 2022

Soojung Lee 1

1경인교육대학교

Accredited

ABSTRACT

The popularity of e-commerce systems on the Internet is increasing day by day, and the recommendation system, as a core function of these systems, greatly reduces the effort to search for desired products by recommending products that customers may prefer. The collaborative filtering technique is a recommendation algorithm that has been successfully implemented in many commercial systems, but despite its popularity and usefulness in academia, the memory-based implementation has inaccuracies in its reference neighbor. To solve this problem, this study proposes a new time-aware collaborative filtering technique that integrates and utilizes the neighbors of each item and each user, weighting the recent similarity more than the past similarity with them, and reflecting it in the recommendation list decision. Through the experimental evaluation, it was confirmed that the proposed method showed superior performance in terms of prediction accuracy than other existing methods.

Citation status

* References for papers published after 2023 are currently being built.