An empirical study of a cross-level association rule mining approach to cold-start recommendations

Cane Wing-ki Leung; Stephen Chan; Fu-Lai Chung

doi:10.1016/j.knosys.2008.03.012

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An empirical study of a cross-level association rule mining approach to cold-start recommendations

Cane Wing-ki Leung Stephen Chan Fu-Lai Chung

Year: 2008 Journal: Knowledge-Based Systems Vol: 21 (7)Pages: 515-529 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2008.03.012

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Keywords:

Cold start (automotive) Collaborative filtering Association rule learning Recommender system Computer science Preference Association (psychology) Domain (mathematical analysis) Empirical research Work (physics) Data mining Data science Information retrieval Machine learning Engineering Psychology Statistics Mathematics

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Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

An empirical study of a cross-level association rule mining approach to cold-start recommendations

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Citation History

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