C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Agents that reduce work and information overload
Communications of the ACM
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction with local patterns using cross-entropy
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Information Sciences: an International Journal
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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We propose a method for induction of compact optimal recommendation policies based on discovery of frequent item-sets in a purchase database, followed by the application of standard decision tree learning algorithms for the purposes of simplification and compaction of the recommendation policies. Experimental results suggest that the structure of such policies can be exploited to partition the space of customer purchasing histories much more efficiently than frequent itemset discovery algorithms alone would allow.