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CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering
February 2011 (vol. 23 no. 2)
pp. 175-189
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
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.

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Index Terms:
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 and Data Engineering, vol. 23, no. 2, pp. 175-189, Feb. 2011, doi:10.1109/TKDE.2010.88
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