GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Nantonac collaborative filtering: recommendation based on order responses
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Personalized pricing recommender system: multi-stage epsilon-greedy approach
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
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A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-scale, have been employed. We replaced such collection methods with a ranking method, in which objects are sorted according to the degree of a user's preference. We developed a technique to convert the rankings to scores based on order statistics theory. This technique successfully improved the accuracy of ranking recommended items. However, we targeted only memory-based recommendation algorithms. To test whether or not the use of ranking methods and our conversion technique are effective for wide variety of recommenders, we apply our conversion technique to model-based algorithms.