Social Web Artifacts for Boosting Recommenders [electronic resource] : Theory and Implementation / by Cai-Nicolas Ziegler.Material type: TextLanguage: English Series: Studies in Computational Intelligence: 487Publisher: Cham : Springer International Publishing : Imprint: Springer, 2013Description: XX, 187 p. 42 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319005270Subject(s): Engineering | Data mining | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Part I Laying Foundations -- Part II Use of Taxonomic Knowledge -- Part III Social Ties and Trust -- Part IV Amalgamating Taxonomies and Trust.
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.