Scalable dimensionality reduction methods for recommender systems
Michal Ciesielczyk
Broschiertes Buch

Scalable dimensionality reduction methods for recommender systems

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In this monograph dimensionality reduction methods and reflective data processing are investigated from the perspective of the ability to produce high precision recommendations and to cope with high unpredictability of the data sparsity. The reported research is oriented on constructing a processing model enabling to provide higher quality recommendations than the state-of-the-art collaborative and content-based filtering methods, but at the same time is not more computationally complex. The results of the theoretical study have been evaluated, according to a well-established methodology, usin...