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A Domain-Independent Framework for Intelligent Recommendations: Design, Application and Evaluation of a Hybrid Machine Learning Framework using Case Studies within varied Domains - Tapa blanda
Sinopsis
Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the internet or when using information systems at their place of work, for instance. As an example, the recommendation techniques collaborative filtering and content-based filtering are mainly applied in the areas of e-Commerce and web navigation to recommend potentially relevant articles or websites. Recommender systems are either based on machine learning functions such as clustering, classification, and prediction or they are realized by symbolic methods like association rule mining, that is, by rule-based mechanisms in general. The hybrid and domain-independent framework developed in this dissertation called SymboConn is based on a recurrent neural network and provides a high generalization capability, flexibility, and robustness. We demonstrate its applicability by case studies in navigation recommendation, design pattern discovery, change impact analysis as well as time series prediction.
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Reseña del editor
Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the internet or when using information systems at their place of work, for instance. As an example, the recommendation techniques collaborative filtering and content-based filtering are mainly applied in the areas of e-Commerce and web navigation to recommend potentially relevant articles or websites. Recommender systems are either based on machine learning functions such as clustering, classification, and prediction or they are realized by symbolic methods like association rule mining, that is, by rule-based mechanisms in general. The hybrid and domain-independent framework developed in this dissertation called SymboConn is based on a recurrent neural network and provides a high generalization capability, flexibility, and robustness. We demonstrate its applicability by case studies in navigation recommendation, design pattern discovery, change impact analysis as well as time series prediction.
Biografía del autor
Dr. Jörn David studied computer science and mathematics at the University of Munich (LMU) from 2001 to 2010. During his stay at the Carnegie Mellon University in 2006, he began his dissertation at the intersection of software engineering and machine learning, which was accomplished at the Technical University of Munich (TUM) in 2009.
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A Domain-Independent Framework for Intelligent Recommendations
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Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG Okt 2015, 2015
ISBN 10: 3838113756
ISBN 13: 9783838113753
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the internet or when using information systems at their place of work, for instance. As an example, the recommendation techniques collaborative filtering and content-based filtering are mainly applied in the areas of e-Commerce and web navigation to recommend potentially relevant articles or websites. Recommender systems are either based on machine learning functions such as clustering, classification, and prediction or they are realized by symbolic methods like association rule mining, that is, by rule-based mechanisms in general. The hybrid and domain-independent framework developed in this dissertation called SymboConn is based on a recurrent neural network and provides a high generalization capability, flexibility, and robustness. We demonstrate its applicability by case studies in navigation recommendation, design pattern discovery, change impact analysis as well as time series prediction. 340 pp. Englisch. Nº de ref. del artículo: 9783838113753
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A Domain-Independent Framework for Intelligent Recommendations
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Südwestdeutscher Verlag für Hochschulschriften, 2015
ISBN 10: 3838113756
ISBN 13: 9783838113753
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the intern. Nº de ref. del artículo: 5405750
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A Domain-Independent Framework for Intelligent Recommendations : Design, Application and Evaluation of a Hybrid Machine Learning Framework using Case Studies within varied Domains
Publicado por
Südwestdeutscher Verlag Für Hochschulschriften, 2010
ISBN 10: 3838113756
ISBN 13: 9783838113753
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the internet or when using information systems at their place of work, for instance. As an example, the recommendation techniques collaborative filtering and content-based filtering are mainly applied in the areas of e-Commerce and web navigation to recommend potentially relevant articles or websites. Recommender systems are either based on machine learning functions such as clustering, classification, and prediction or they are realized by symbolic methods like association rule mining, that is, by rule-based mechanisms in general. The hybrid and domain-independent framework developed in this dissertation called SymboConn is based on a recurrent neural network and provides a high generalization capability, flexibility, and robustness. We demonstrate its applicability by case studies in navigation recommendation, design pattern discovery, change impact analysis as well as time series prediction. Nº de ref. del artículo: 9783838113753
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A Domain-Independent Framework for Intelligent Recommendations | Design, Application and Evaluation of a Hybrid Machine Learning Framework using Case Studies within varied Domains
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Südwestdeutscher Verlag für Hochschulschriften, 2015
ISBN 10: 3838113756
ISBN 13: 9783838113753
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Taschenbuch. Condición: Neu. A Domain-Independent Framework for Intelligent Recommendations | Design, Application and Evaluation of a Hybrid Machine Learning Framework using Case Studies within varied Domains | Jörn David | Taschenbuch | 340 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838113753 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 101312575
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A Domain-Independent Framework for Intelligent Recommendations
Publicado por
Südwestdeutscher Verlag Für Hochschulschriften Jan 2010, 2010
ISBN 10: 3838113756
ISBN 13: 9783838113753
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recommender systems assist the user in decision- making processes and automate information processing steps like the classification of artifacts. Intelligent recommendations help users to cope with the steadily growing information overload within the internet or when using information systems at their place of work, for instance. As an example, the recommendation techniques collaborative filtering and content-based filtering are mainly applied in the areas of e-Commerce and web navigation to recommend potentially relevant articles or websites. Recommender systems are either based on machine learning functions such as clustering, classification, and prediction or they are realized by symbolic methods like association rule mining, that is, by rule-based mechanisms in general. The hybrid and domain-independent framework developed in this dissertation called SymboConn is based on a recurrent neural network and provides a high generalization capability, flexibility, and robustness. We demonstrate its applicability by case studies in navigation recommendation, design pattern discovery, change impact analysis as well as time series prediction.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 340 pp. Englisch. Nº de ref. del artículo: 9783838113753
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