Overview
- Shows how to use Web Knowledge for Boosting Recommenders
- Presents trust and classification taxonomies for recommender systems
- Written by an expert in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 487)
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Table of contents (10 chapters)
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Front Matter
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Laying Foundations
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Front Matter
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Use of Taxonomic Knowledge
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Front Matter
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Social Ties and Trust
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Front Matter
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Amalgamating Taxonomies and Trust
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Social Web Artifacts for Boosting Recommenders
Book Subtitle: Theory and Implementation
Authors: Cai-Nicolas Ziegler
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-00527-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2013
Hardcover ISBN: 978-3-319-00526-3Published: 31 May 2013
Softcover ISBN: 978-3-319-03287-0Published: 16 May 2015
eBook ISBN: 978-3-319-00527-0Published: 23 April 2013
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIX, 187
Topics: Computational Intelligence, Artificial Intelligence, Data Mining and Knowledge Discovery