Von dem Buch Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) haben wir 3 gleiche oder sehr ähnliche Ausgaben identifiziert!
Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
100%: Ziegler, Cai-Nicolas: Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (ISBN: 9783319032870) 2015, 2013. Ausgabe, in Englisch, Band: 487, Taschenbuch.Nur diese Ausgabe anzeigen…
100%: Cai-Nicolas Ziegler: Social Web Artifacts for Boosting Recommenders (ISBN: 9783319005270) in Deutsch, Taschenbuch.Nur diese Ausgabe anzeigen…
80%: Ziegler, Cai Nicolas and Ziegler, Pd Dr Cai: Gebr. Social Web Artifacts for Boosting Recommenders: Theory Implementation (Studies in Computational Intelligence) (ISBN: 9783319005263) 2013, Springer, 2013. Ausgabe, in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence)
18 Angebote vergleichen
Bester Preis: Fr. 100.41 (€ 102.60)¹ (vom 19.05.2018)1
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (2013)
DE HC USISBN: 9783319005263 bzw. 331900526X, in Deutsch, Springer, gebundenes Buch, gebraucht.
Von Händler/Antiquariat, HPB-Ohio [56525498], Dallas, TX, U.S.A.
Item may show signs of shelf wear. Pages may include limited notes and highlighting. May include supplemental or companion materials if applicable. Access codes may or may not work. Connecting readers since 1972. Customer service is our top priority.
Item may show signs of shelf wear. Pages may include limited notes and highlighting. May include supplemental or companion materials if applicable. Access codes may or may not work. Connecting readers since 1972. Customer service is our top priority.
2
Symbolbild
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Paperback) (2015)
DE PB NW RPISBN: 9783319032870 bzw. 3319032879, in Deutsch, Springer International Publishing AG, Switzerland, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the Web 2.0 or the Social Web : Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders scalability problem.
Language: English Brand New Book ***** Print on Demand *****.Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the Web 2.0 or the Social Web : Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders scalability problem.
3
Symbolbild
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (2015)
DE USISBN: 9783319032870 bzw. 3319032879, in Deutsch, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkosten nach: DEU.
Von Händler/Antiquariat, Books Express.
2015-05-19. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!
Von Händler/Antiquariat, Books Express.
2015-05-19. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!
4
Social Web Artifacts for Boosting Recommenders
DE NWISBN: 9783319005263 bzw. 331900526X, in Deutsch, neu.
Lieferung aus: Deutschland, 2-3 Werktage.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. von Ziegler, Cai-Nicolas, Neu.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. von Ziegler, Cai-Nicolas, Neu.
5
| Social Web Artifacts for Boosting Recommenders | Springer | 2013
~EN NWISBN: 9783319005263 bzw. 331900526X, vermutlich in Englisch, Springer, neu.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the Web 2.0 or the Social Web: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties when used as proxies for interest similarity are able to mitigate the recommenders' scalability problem.
6
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (2013)
EN HC USISBN: 9783319005263 bzw. 331900526X, in Englisch, 187 Seiten, 2013. Ausgabe, Springer, gebundenes Buch, gebraucht.
Neu ab: $157.99 (15 Angebote)
Gebraucht ab: $125.09 (8 Angebote)
Zu den weiteren 23 Angeboten bei Amazon.com
Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 24 hours, tatsächliche Versandkosten können abweichen.
Von Händler/Antiquariat, HPB-Ohio.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties – when used as proxies for interest similarity – are able to mitigate the recommenders' scalability problem., Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-04-23, Studio: Springer, Verkaufsrang: 8919987.
Von Händler/Antiquariat, HPB-Ohio.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties – when used as proxies for interest similarity – are able to mitigate the recommenders' scalability problem., Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-04-23, Studio: Springer, Verkaufsrang: 8919987.
7
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (2013)
EN HC NWISBN: 9783319005263 bzw. 331900526X, in Englisch, 208 Seiten, 2013. Ausgabe, Springer, gebundenes Buch, neu.
Neu ab: £106.02 (18 Angebote)
Gebraucht ab: £82.08 (3 Angebote)
Zu den weiteren 21 Angeboten bei Amazon.co.uk
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Usually dispatched within 3-4 business days, zzgl. Versandkosten (wenn der Anbieter in Ihr Land liefert).
Von Händler/Antiquariat, UKPaperbackshop.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-05-31, Freigegeben: 2013-05-31, Studio: Springer.
Von Händler/Antiquariat, UKPaperbackshop.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-05-31, Freigegeben: 2013-05-31, Studio: Springer.
8
Social Web Artifacts for Boosting Recommenders: Theory and Implementation (Studies in Computational Intelligence) (2013)
EN HC USISBN: 9783319005263 bzw. 331900526X, in Englisch, 208 Seiten, 2013. Ausgabe, Springer, gebundenes Buch, gebraucht.
Neu ab: £106.02 (18 Angebote)
Gebraucht ab: £82.08 (3 Angebote)
Zu den weiteren 21 Angeboten bei Amazon.co.uk
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Usually dispatched within 3-4 business days, zzgl. Versandkosten (wenn der Anbieter in Ihr Land liefert).
Von Händler/Antiquariat, sales-de.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-05-31, Freigegeben: 2013-05-31, Studio: Springer.
Von Händler/Antiquariat, sales-de.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Hardcover, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2013-05-31, Freigegeben: 2013-05-31, Studio: Springer.
9
Social Web Artifacts for Boosting Recommenders (Studies in Computational Intelligence) (2015)
EN PB USISBN: 9783319032870 bzw. 3319032879, in Englisch, 208 Seiten, 2013. Ausgabe, Springer, Taschenbuch, gebraucht.
Neu ab: £89.23 (12 Angebote)
Gebraucht ab: £104.60 (2 Angebote)
Zu den weiteren 14 Angeboten bei Amazon.co.uk
Lieferung aus: Vereinigtes Königreich Grossbritannien und Nordirland, Usually dispatched within 1-2 business days, zzgl. Versandkosten (wenn der Anbieter in Ihr Land liefert).
Von Händler/Antiquariat, swestbooks.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Paperback, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2015-05-20, Freigegeben: 2015-05-20, Studio: Springer.
Von Händler/Antiquariat, swestbooks.
Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes. At the same time, a new evolution on the Web has started to take shape, commonly known as the "Web 2.0" or the "Social Web": Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people. This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties - when used as proxies for interest similarity - are able to mitigate the recommenders' scalability problem. Paperback, Ausgabe: 2013, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2015-05-20, Freigegeben: 2015-05-20, Studio: Springer.
10
Social Web Artifacts for Boosting Recommenders
DE PB NWISBN: 9783319005270 bzw. 3319005278, in Deutsch, Springer International Publishing, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Lade…