Nantonac collaborative filtering: a model-based approach

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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information filtering General Terms Measurement Keywords Recommender System, Ranking Method, Order Statistics, Sensory Test

• Type: Conference
• Year: 2010
• Where: RECSYS
• Authors: Toshihiro Kamishima, Shotaro Akaho
• Keywords: Control Systems, Conversion Technique, Ranking Method, Recommender System, RECSYS 2010