uda ryuya (25 risultati)

Hardcover. Condizione: Very Good. No Jacket. 1st Edition. 2008.Hardcover.Very good condition.363 pages.Ships from Japan.Usually ships in 1-2 working days.

Hardcover. Condizione: Very Good. 2008. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condizione: Very Good. 2008. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condizione: Good.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. pp. 392.

Condizione: New. In.

Paperback. Condizione: Brand New. 366 pages. 9.00x6.00x0.89 inches. In Stock.

Hardcover. Condizione: Brand New. 1st edition. 366 pages. 9.21x6.14x0.88 inches. In Stock.

Taschenbuch. Condizione: Neu. Towards Sustainable Society on Ubiquitous Networks | The 8th IFIP Conference on e-Business, e-Services, and e-Society (I3E 2008), September 24 - 26, 2008, Tokyo, Japan | Makoto Oya (u. a.) | Taschenbuch | IFIP Advances in Information and Communication Technology | xxvi | Englisch | 2010 | Springer | EAN 9781441946720 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The massive growth of the Internet has made an enormous amount of infor- tion available to us. However, it is becoming very difficult for users to acquire an - plicable one. Therefore, some techniques such as information filtering have been - troduced to address this issue. Recommender systems filter information that is useful to a user from a large amount of information. Many e-commerce sites use rec- mender systems to filter specific information that users want out of an overload of - formation [2]. For example, Amazon. com is a good example of the success of - commender systems [1]. Over the past several years, a considerable amount of research has been conducted on recommendation systems. In general, the usefulness of the recommendation is measured based on its accuracy [3]. Although a high - commendation accuracy can indicate a user's favorite items, there is a fault in that - ly similar items will be recommended. Several studies have reported that users might not be satisfied with a recommendation even though it exhibits high recommendation accuracy [4]. For this reason, we consider that a recommendation having only accuracy is - satisfactory. The serendipity of a recommendation is an important element when c- sidering a user's long-term profits. A recommendation that brings serendipity to users would solve the problem of 'user weariness' and would lead to exploitation of users' tastes. The viewpoint of the diversity of the recommendation as well as its accuracy should be required for future recommender systems.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 363 | Sprache: Englisch | Produktart: Bücher | International Federation for Information ProcessingThe IFIP series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of refereed international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP series is to encourage education and the dissemination and exchange of information about all aspects of computing.For more information about the 300 other books in the IFIP series, please visit [LINK ENTFERNT] more information about IFIP, please visit [LINK ENTFERNT].

Paperback. Condizione: Like New. Like New. book.

Condizione: new. Questo è un articolo print on demand.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The massive growth of the Internet has made an enormous amount of infor- tion available to us. However, it is becoming very difficult for users to acquire an - plicable one. Therefore, some techniques such as information filtering have been - troduced to address this issue. Recommender systems filter information that is useful to a user from a large amount of information. Many e-commerce sites use rec- mender systems to filter specific information that users want out of an overload of - formation [2]. For example, Amazon. com is a good example of the success of - commender systems [1]. Over the past several years, a considerable amount of research has been conducted on recommendation systems. In general, the usefulness of the recommendation is measured based on its accuracy [3]. Although a high - commendation accuracy can indicate a user's favorite items, there is a fault in that - ly similar items will be recommended. Several studies have reported that users might not be satisfied with a recommendation even though it exhibits high recommendation accuracy [4]. For this reason, we consider that a recommendation having only accuracy is - satisfactory. The serendipity of a recommendation is an important element when c- sidering a user's long-term profits. A recommendation that brings serendipity to users would solve the problem of 'user weariness' and would lead to exploitation of users' tastes. The viewpoint of the diversity of the recommendation as well as its accuracy should be required for future recommender systems. 392 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The papers in this volume were peer-reviewed and carefully selectedMuch information in this series is published in advance of journal publicationThe contributors in this volume are world-renowned experts in their fieldThe massive gr.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The papers in this volume were peer-reviewed and carefully selectedMuch information in this series is published in advance of journal publicationThe contributors in this volume are world-renowned experts in their fieldThe massive gr.

Condizione: New. Print on Demand pp. 392 104 Illus.

Condizione: New. PRINT ON DEMAND pp. 392.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The massive growth of the Internet has made an enormous amount of infor- tion available to us. However, it is becoming very difficult for users to acquire an - plicable one. Therefore, some techniques such as information filtering have been - troduced to address this issue. Recommender systems filter information that is useful to a user from a large amount of information. Many e-commerce sites use rec- mender systems to filter specific information that users want out of an overload of - formation [2]. For example, Amazon. com is a good example of the success of - commender systems [1]. Over the past several years, a considerable amount of research has been conducted on recommendation systems. In general, the usefulness of the recommendation is measured based on its accuracy [3]. Although a high - commendation accuracy can indicate a user's favorite items, there is a fault in that - ly similar items will be recommended. Several studies have reported that users might not be satisfied with a recommendation even though it exhibits high recommendation accuracy [4]. For this reason, we consider that a recommendation having only accuracy is - satisfactory. The serendipity of a recommendation is an important element when c- sidering a user's long-term profits. A recommendation that brings serendipity to users would solve the problem of ¿user weariness¿ and would lead to exploitation of users' tastes. The viewpoint of the diversity of the recommendation as well as its accuracy should be required for future recommender systems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 392 pp. Englisch.