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001 978-3-319-00527-0
003 DE-He213
005 20200421111652.0
007 cr nn 008mamaa
008 130424s2013 gw | s |||| 0|eng d
020 _a9783319005270
_9978-3-319-00527-0
024 7 _a10.1007/978-3-319-00527-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aZiegler, Cai-Nicolas.
_eauthor.
245 1 0 _aSocial Web Artifacts for Boosting Recommenders
_h[electronic resource] :
_bTheory and Implementation /
_cby Cai-Nicolas Ziegler.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIX, 187 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v487
505 0 _aPart I Laying Foundations -- Part II Use of Taxonomic Knowledge -- Part III Social Ties and Trust -- Part IV Amalgamating Taxonomies and Trust.
520 _aRecommender 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.
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319005263
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v487
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00527-0
912 _aZDB-2-ENG
942 _cEBK
999 _c54459
_d54459