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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Bibliographic Details
Published in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Vol. 3; pp. 147 - 150
Main Authors: Braak, Paul te, Abdullah, Noraswaliza, Xu, Yue
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Series:ACM Conferences
Subjects:
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
Online Access:Get full text
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Summary: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 minded users have previously rated items. Such methods require large amounts of training data which highlights a scalability problem of collaborative filtering, namely, the trade-off between accurate estimation prediction and the time required to calculate them. This paper demonstrates a novel approach to determine interest thus improving scalability by partitioning training data into user based profile clusters. The partitioned data represents user segments (or profile types) which is used to as a more concise representation of similar users for the target. Experimental results have shown a dramatic increase in prediction speed without a loss in accuracy.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.422