Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Communications of the ACM
Recommender systems for evaluating computer messages
Communications of the ACM
Predictors of online buying behavior
Communications of the ACM
Web usage mining for Web site evaluation
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Predicting customer shopping lists from point-of-sale purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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In this contribution we transfer a customer purchase incidence model for consumer products which is based on Ehrenberg's repeat-buying theory to Web-based information products. Ehrenberg's repeat-buying theory successfully describes regularities on a large number of consumer product markets. We show that these regularities exist in electronic markets for information goods, too, and that purchase incidence models provide a well founded theoretical base for recommender and alert services.The article consists of two parts. In the first part Ehrenberg's repeat-buying theory and its assumptions are reviewed and adapted for web-based information markets. Second, we present the empirical validation of the model based on data collected from the information market of the Virtual University of the Vienna University of Economics and Business Administration from September 1999 to May 2001.