| 摘要 |
Disclosed herein are systems and methods for supplementing content-based attributes with collaborative rating attributes for recommending or filtering items. Collaborative rating data may be consolidated into composite critics which serve as item quality rating attributes. These attributes may be used in conjunction with content-based attributes to generate user preference models. Composite critics may be formed using data clustering methods such that users with similar tastes may be grouped together. The user preference models may be induced using machine learning processes, such as decision trees, artificial neural networks, support vector machines, and/or statistical techniques. In some embodiments, composite critics may represent a small number of users or professional critics selected for having differing sensibilities and who rate most or all items according to those sensibilities.
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