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WWW7 Proceedings of the seventh international conference on World Wide Web 7
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Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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iTrust'05 Proceedings of the Third international conference on Trust Management
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
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ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
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Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
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CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
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Knowledge-Based Systems
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ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Expert Systems with Applications: An International Journal
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Multimedia Tools and Applications
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Information Processing and Management: an International Journal
Automatic news recommendations via aggregated profiling
Multimedia Tools and Applications
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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
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MIS Quarterly
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Expert Systems with Applications: An International Journal
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AI Communications - Intelligent Engineering Techniques for Knowledge Bases
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An evaluation of six recommendation algorithms on e-commerce-related data sets is an initial step toward a metalevel guideline for choosing the best algorithm for a given application with certain data characteristics. Two of the evaluated algorithms are the popular user-based and item-based correlation/similarity algorithms. The other four algorithms attempt to meet the challenge of data sparsity through dimensionality reduction, generative models, spreading activation, or link analysis. Initial experimental comparisons indicate that the link-analysis algorithm achieves the best overall performance across several e-commerce data sets.