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Synopsis
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, using publicly available data sets and following scenarios reflecting the so-called find-good-items task (rather than the low-error-of-ratings prediction). Based on the presented analysis and experimental results, the author states that vector-space recommendation techniques and dimensionality reduction methods may be combined in a way preserving the high quality of recommendations, regardless of the amount of processed heterogeneous data.
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About the Author
Michał Ciesielczyk received his PhD degree in computer science from Poznan University of Technology in 2015. Currently, he is an assistant professor at the Institute of Control and Information Engineering at the Poznan University of Technology. His research focuses on information retrieval, recommender systems, and statistical relational learning.
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Scalable dimensionality reduction methods for recommender systems
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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, using publicly available data sets and following scenarios reflecting the so-called find-good-items task (rather than the low-error-of-ratings prediction). Based on the presented analysis and experimental results, the author states that vector-space recommendation techniques and dimensionality reduction methods may be combined in a way preserving the high quality of recommendations, regardless of the amount of processed heterogeneous data. 208 pp. Englisch. Seller Inventory # 9783659836756
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Scalable dimensionality reduction methods for recommender systems
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ciesielczyk MichalMichal Ciesielczyk received his PhD degree in computer science from Poznan University of Technology in 2015. Currently, he is an assistant professor at the Institute of Control and Information Engineering at the Poz. Seller Inventory # 151428875
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Taschenbuch. Condition: Neu. Scalable dimensionality reduction methods for recommender systems | Micha¿ Ciesielczyk | Taschenbuch | 208 S. | Englisch | 2016 | Scholars' Press | EAN 9783659836756 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 103867021
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Scalable dimensionality reduction methods for recommender systems
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, using publicly available data sets and following scenarios reflecting the so-called find-good-items task (rather than the low-error-of-ratings prediction). Based on the presented analysis and experimental results, the author states that vector-space recommendation techniques and dimensionality reduction methods may be combined in a way preserving the high quality of recommendations, regardless of the amount of processed heterogeneous data.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch. Seller Inventory # 9783659836756
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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, using publicly available data sets and following scenarios reflecting the so-called find-good-items task (rather than the low-error-of-ratings prediction). Based on the presented analysis and experimental results, the author states that vector-space recommendation techniques and dimensionality reduction methods may be combined in a way preserving the high quality of recommendations, regardless of the amount of processed heterogeneous data. Seller Inventory # 9783659836756
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