Improving the Performance of Collaborative Filtering Recommender Systems through User Profile Clustering

Recommender systems offer personalization to online activities due to their ability to recommend products that are unknown to the user. The most common form of these systems employs collaborative filtering to make recommendations and operate by estimating a preference for an item based on how like m...

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Vydáno v:	Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Ročník 3; s. 147 - 150
Hlavní autoři:	Braak, Paul te, Abdullah, Noraswaliza, Xu, Yue
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	Washington, DC, USA IEEE Computer Society 15.09.2009 IEEE
Edice:	ACM Conferences
Témata:	Collaborative Filtering Collaborative work Computing methodologies > Machine learning > Learning settings Conferences Data Mining Human-centered computing > Collaborative and social computing Information filtering Information filters Information systems > Information retrieval > Evaluation of retrieval results Information systems > Information retrieval > Retrieval tasks and goals > Document filtering Information systems > Information retrieval > Retrieval tasks and goals > Information extraction Information systems > Information retrieval > Users and interactive retrieval > Personalization Information systems > Information systems applications > Data mining Information systems > World Wide Web > Web searching and information discovery > Personalization Intelligent agent International collaboration Knowledge Acquisition Online Communities/Technical Collaboration Recommender systems Scalability Training data Web Intelligence Data Mining Collaborative Filtering Knowledge Acquisition Web Intelligence
ISBN:	0769538010, 9780769538013
On-line přístup:	Získat plný text
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