Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
International Journal of Electronic Commerce
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In this paper, we propose a data mining approach to recommending new library books that have never been rated or borrowed by users. In our problem context, users are characterized by their demographic attributes, and concept hierarchies can be defined for some of these demographic attributes. Books are assigned to the base categories of a taxonomy. Our goal is therefore to identify the type of users interested in some specific type of books. We call such knowledge generalized profile association rules. In this paper, we propose a new definition of rule interestingness to prune away rules that are redundant and not useful in book recommendation. We have developed a new algorithm for efficiently discovering generalized profile association rules from a circulation database. It is noted that generalized profile association rules can be applied to other kinds of applications, including e-commerce.