Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
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Most existing data mining approaches to e-commerce recommendation are past data model-based in the sense that they first build a preference model from a past dataset and then apply the model to current customer situations. Such approaches are not suitable for applications where fresh data should be collected instantly since it reflects changes to customer preferences over some products. This paper targets those e-commerce environments in which knowledge of customer preferences may change frequently. But due to the very large size of past datasets the preference models cannot be updated instantly in response to the changes. We present an approach to making real time online recommendations based on an up-to-moment dataset which includes not only a gigantic past dataset but the most recent data that may be collected just moments ago.