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• W1567325480 abstract "Recommender systems apply intelligent access technologies to large information systems. These systems, especially collaborative filtering based ones, are achieving widespread success on the Web. In recent years, the amount of available information and the number of visitors to Web sites are increasing enormously. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale information resources. In this paper we apply inductive learning algorithm to the recommendation process. Instead of computing user-user or item-item similarities, we construct a decision tree to represent user preference. Recommendations are performed by decision tree classification. To inspect the effectiveness of this technology, we set up a movie recommender system based on inductive learning and make online experiments for evaluation. Our results suggest that inductive-learning-based technology is promising for the solution of the very large-scale problems and high-quality recommendations can be expected." @default.
• W1567325480 created "2016-06-24" @default.
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• W1567325480 date "2005-07-06" @default.
• W1567325480 modified "2023-09-26" @default.
• W1567325480 title "A movie recommender system based on inductive learning" @default.
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• W1567325480 doi "https://doi.org/10.1109/iccis.2004.1460433" @default.
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