Rich Thompson

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In this paper, we apply stacking, an ensemble learning method, to the problem of building hybrid recommendation systems. We also introduce the novel idea of using runtime metrics which represent properties of the input users/items as additional meta-features, allowing us to combine component recommendation engines at runtime based on user/item(More)
This paper presents a taxonomy of recommender systems with the goal of assisting in selection and application of these systems. Recommendation methods are usually classified into three main categories: collaborative,contentbased, and knowledge-based. We outline a taxonomy of recommender systems based on problem characteristics and the underlying technology.(More)
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