A collaborative filtering recommendation system with dynamic time decay

Yi Cheng Chen, Lin Hui, Tipajin Thaipisutikul

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people’s preferences usually vary with time; the traditional CF could not properly reveal the change in users’ interests. In this paper, we propose a novel CF-based recommendation, dynamic decay collaborative filtering (DDCF), which captures the preference variations of users and includes the concept of dynamic time decay. We extend the idea of human brain memory to specify the level of a user’s interests (i.e., instantaneous, short-term, or long-term). According to different interest levels, DDCF dynamically tunes the decay function based on users’ behaviors. The experimental results show that DDCF with the integration of the dynamic decay concept performs better than traditional CF. In addition, we conduct experiments on real-world datasets to demonstrate the practicability of the proposed DDCF.

Original languageEnglish
Pages (from-to)244-262
Number of pages19
JournalJournal of Supercomputing
Issue number1
StatePublished - Jan 2021


  • Collaborative filtering
  • Decay function
  • Human brain memory
  • Recommendation system


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