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EBOOK
Author Ziegler, Cai-Nicolas.
Title Social web artifacts for boosting recommenders : theory and implementation / Cai-Nicolas Ziegler.
Imprint Cham ; New York : Springer, 2013.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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Series Studies in computational intelligence, 1860-949X ; v. 487
Studies in computational intelligence ; v. 487.
Subject Recommender systems (Information filtering)
Data mining.
Engineering.
Data mining.
Artificial intelligence.
Computational Intelligence.
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Studies in computational intelligence, 1860-949X ; v. 487
Studies in computational intelligence ; v. 487.
Subject Recommender systems (Information filtering)
Data mining.
Engineering.
Data mining.
Artificial intelligence.
Computational Intelligence.
Description 1 online resource.
Contents Laying Foundations -- Introduction -- On Recommender Systems -- Use of Taxonomic Knowledge -- Taxonomy-Driven Filtering -- Topic Diversification Revisited -- Taxonomies for Calculating Semantic Proximity -- Recommending Technology Synergies -- Social Ties and Trust -- Trust Propagation Models -- Interpersonal Trust and Similarity -- Amalgamating Taxonomies and Trust -- Decentralized Recommender Systems -- Conclusion.
Bibliography Note Includes bibliographical references.
Summary 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.
Note English.
ISBN 9783319005270 (electronic bk.)
3319005278 (electronic bk.)
331900526X (print)
9783319005263 (print)
9783319005263
ISBN/ISSN 10.1007/978-3-319-00527-0
OCLC # 841929921
Additional Format Printed edition: 9783319005263