CoFiDS: A belief-theoretic approach for automated collaborative filtering

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be avoided, designers resort to simplifying assumptions that impair the system's performance and utility. We have developed a novel technique referred to as CoFiDSCollaborative Filtering based on Dempster-Shafer belief-theoretic frameworkthat can represent a wide variety of data imperfections, propagate them throughout the decision-making process without the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the domain expert can either obtain a "hard decision or can narrow the set of possible predictions to a smaller set. With its capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as medical decision support systems and defense-related applications. We describe the theoretical foundation of the system and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS' behavior and show that its performance compares favorably with other ACF systems.

Original languageEnglish (US)
Article number5467080
Pages (from-to)175-189
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
StatePublished - 2011


  • ambiguous data
  • collaborative filtering
  • contextual information
  • Dempster-Shafer (DS) theory
  • imperfect data
  • Recommender systems
  • user preference modeling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications


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