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

ISBN 10: 3838113756 / ISBN 13: 9783838113753

Editore: Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG, 2010

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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. Codice articolo 9783838113753

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Titolo: A Domain-Independent Framework for ...

Casa editrice: Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG

Data di pubblicazione: 2010

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

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.

L'autore:

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.