2013
DOI: 10.3233/his-130166
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A fuzzy co-clustering approach for hybrid recommender systems

Abstract: Many efforts have been done to tackle the problem of information abundance in the World Wide Web. Growth in the number of web users and the necessity of making the information available on the web make web recommender systems very critical and popular. Recommender systems use the knowledge obtained through the analysis of users' navigational behavior to customize a web site to the needs of each particular user or set of users. Most of the existing recommender systems use either content-based or collaborative f… Show more

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Cited by 19 publications
(8 citation statements)
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“…The objective function of the fuzzy c‐means algorithm is reformulated with an exponential term to give lower membership values to less relevant data points. Forsati et al . propose a fuzzy clustering approach to recommend web pages to users, utilizing web content data as a set of keywords to define the similarity between web pages and generating user profiles from their browsing history.…”
Section: Related Workmentioning
confidence: 99%
“…The objective function of the fuzzy c‐means algorithm is reformulated with an exponential term to give lower membership values to less relevant data points. Forsati et al . propose a fuzzy clustering approach to recommend web pages to users, utilizing web content data as a set of keywords to define the similarity between web pages and generating user profiles from their browsing history.…”
Section: Related Workmentioning
confidence: 99%
“…Vadrevu et al [2] propose an incremental version of k-means clustering algorithm, suited for dynamic sets of documents by incrementally reviewing already detected topics after a given number of new documents is introduced into the documents set. Forsati et al [3] propose a clustering based approach to content analysis utilized in a recommender system.…”
Section: Topic Detectionmentioning
confidence: 99%
“…Categories of GAs are -simple GA (SGA) and multi objective GA (MOGA). Although clustering is a MOOP, in [8,19,24,32,34,50,52,58,65] researchers used SGA in clustering. Most of them optimized single objective or they have converted many objectives into single objective, which is hardly equally applicable to all kinds of data sets.…”
Section: Previous Workmentioning
confidence: 99%