DocumentCode :
1496457
Title :
CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering
Author :
Wickramarathne, Thanuka L. ; Premaratne, Kamal ; Kubat, Miroslav ; Jayaweera, Dushyantha T.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA
Volume :
23
Issue :
2
fYear :
2011
Firstpage :
175
Lastpage :
189
Abstract :
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.
Keywords :
belief maintenance; electronic commerce; groupware; inference mechanisms; recommender systems; CoFiDS; Dempster-Shafer belief theoretic framework; automated collaborative filtering; belief theoretic approach; e-commerce applications; medical decision support systems; recommender systems; Collaboration; Decision making; Decision support systems; Filtering algorithms; Information filtering; Information filters; Information retrieval; Recommender systems; System performance; User interfaces; Dempster-Shafer (DS) theory; Recommender systems; ambiguous data; collaborative filtering; contextual information.; imperfect data; user preference modeling;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2010.88
Filename :
5467080
Link To Document :
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