000 02984nam a22004455i 4500
001 978-3-642-16898-7
003 DE-He213
005 20140220083750.0
007 cr nn 008mamaa
008 101118s2011 gw | s |||| 0|eng d
020 _a9783642168987
_9978-3-642-16898-7
024 7 _a10.1007/978-3-642-16898-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aRendle, Steffen.
_eauthor.
245 1 0 _aContext-Aware Ranking with Factorization Models
_h[electronic resource] /
_cby Steffen Rendle.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXII, 180p. 30 illus.
_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 ;
_v330
505 0 _aPart I Overview -- Part II Theory -- Part III Application -- Part IV Extensions -- Part V Conclusion.
520 _aContext-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642168970
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v330
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-16898-7
912 _aZDB-2-ENG
999 _c107212
_d107212