An empirical study of a cross-level association rule mining approach to cold-start recommendations
- 31 October 2008
- journal article
- Published by Elsevier BV in Knowledge-Based Systems
- Vol. 21 (7), 515-529
- https://doi.org/10.1016/j.knosys.2008.03.012
Abstract
No abstract availableKeywords
This publication has 23 references indexed in Scilit:
- A new approach for combining content-based and collaborative filtersJournal of Intelligent Information Systems, 2006
- A collaborative filtering framework based on fuzzy association rules and multiple-level similarityKnowledge and Information Systems, 2006
- CROC: A New Evaluation Criterion for Recommender SystemsElectronic Commerce Research, 2005
- Evaluating collaborative filtering recommender systemsACM Transactions on Information Systems, 2004
- Interestingness Measures for Fuzzy Association RulesLecture Notes in Computer Science, 2001
- Scalable algorithms for association miningIEEE Transactions on Knowledge and Data Engineering, 2000
- Mining multiple-level association rules in large databasesIEEE Transactions on Knowledge and Data Engineering, 1999
- GroupLensCommunications of the ACM, 1997
- FabCommunications of the ACM, 1997
- Using collaborative filtering to weave an information tapestryCommunications of the ACM, 1992