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

Edité par Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG, 2010

A propos de cet article

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.

N° de réf. du vendeur 9783838113753

Détails bibliographiques

• Titre: A Domain-Independent Framework for Intelligent Recommendations : Design, Application and Evaluation of a Hybrid Machine Learning Framework using Case Studies within varied Domains
• Éditeur: Südwestdeutscher Verlag Für Hochschulschriften AG Co. KG
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 3838113756
• ISBN à 13 chiffres: 9783838113753
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 220 L x 150 H x 21 P (en millimètres) 220 millimètres de largeur par 150 millimètres de hauteur par 21 millimètres de profondeur
• Poids: 524 grammes

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Synopsis

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.