Issue No.02 - February (2011 vol.23)
Thanuka L. Wickramarathne , University of Miami, Coral Gables
Kamal Premaratne , University of Miami, Coral Gables
Miroslav Kubat , University of Miami, Coral Gables
Dushyantha T. Jayaweera , University of Miami, Coral Gables
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.88
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 CoFiDS—Collaborative Filtering based on Dempster-Shafer belief-theoretic framework—that 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.
Recommender systems, collaborative filtering, Dempster-Shafer (DS) theory, imperfect data, ambiguous data, user preference modeling, contextual information.
Thanuka L. Wickramarathne, Kamal Premaratne, Miroslav Kubat, Dushyantha T. Jayaweera, "CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 2, pp. 175-189, February 2011, doi:10.1109/TKDE.2010.88