Multimedia Services in Intelligent Environments [electronic resource] : Advances in Recommender Systems / edited by George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain.

By: Tsihrintzis, George A [editor.]Contributor(s): Virvou, Maria [editor.] | Jain, Lakhmi C [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Smart Innovation, Systems and Technologies: 24Publisher: Heidelberg : Springer International Publishing : Imprint: Springer, 2013Description: X, 174 p. 42 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319003726Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Multimedia Services in Intelligent Environments – Advances in Recommender Systems -- A Survey of Approaches to Designing Recommender Systems -- Hybrid User Model for Capturing a User's Information Seeking Intent -- Recommender Systems: Network Approaches -- Toward the Next Generation of Recommender Systems: Applications and Research Challenges -- Pattern Extraction from Graphs and Beyond.
In: Springer eBooksSummary: Multimedia services are now commonly used in various activities in the daily lives of humans. Related application areas include services that allow access to large depositories of information, digital libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal services, as well as their mobile counterparts (i.e., m-services). Despite the tremendous growth of multimedia services over the recent years, there is an increasing demand for their further development. This demand is driven by the ever-increasing desire of society for easy accessibility to information in friendly, personalized and adaptive environments. In this book at hand, we examine recent Advances in Recommender Systems. Recommender systems are crucial in multimedia services, as they aim at protecting the service users from information overload. The book includes nine chapters, which present various recent research results in recommender systems. This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommender systems, advancing the corresponding state of the art and developing recommender systems for specific applications.  
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Multimedia Services in Intelligent Environments – Advances in Recommender Systems -- A Survey of Approaches to Designing Recommender Systems -- Hybrid User Model for Capturing a User's Information Seeking Intent -- Recommender Systems: Network Approaches -- Toward the Next Generation of Recommender Systems: Applications and Research Challenges -- Pattern Extraction from Graphs and Beyond.

Multimedia services are now commonly used in various activities in the daily lives of humans. Related application areas include services that allow access to large depositories of information, digital libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal services, as well as their mobile counterparts (i.e., m-services). Despite the tremendous growth of multimedia services over the recent years, there is an increasing demand for their further development. This demand is driven by the ever-increasing desire of society for easy accessibility to information in friendly, personalized and adaptive environments. In this book at hand, we examine recent Advances in Recommender Systems. Recommender systems are crucial in multimedia services, as they aim at protecting the service users from information overload. The book includes nine chapters, which present various recent research results in recommender systems. This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommender systems, advancing the corresponding state of the art and developing recommender systems for specific applications.  

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