GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Self-organizing maps
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining Market Basket Data Using Share Measures and Characterized Itemsets
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
A logical approach to case-based reasoning using fuzzy similarity relations
Information Sciences: an International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Two-way cooperative prediction for collaborative filtering recommendations
Expert Systems with Applications: An International Journal
Evaluating the Jaccard-Tanimoto Index on Multi-core Architectures
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
REQUEST: A Query Language for Customizing Recommendations
Information Systems Research
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A strategy-oriented operation module for recommender systems in E-commerce
Computers and Operations Research
Co-clustering with augmented matrix
Applied Intelligence
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Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative Filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. The fundamental assumption of such algorithms resides in the available similarity information between a specific active user and a database of all other users. We study the effects of different similarity measures, available data points per user and the number of items to be recommended on the relative predictive performance in an experiment using market basket data collected from a grocery retailer. Using various measures for evaluation of the predictive ability, we derive some clues to the proper parameterization of such systems.