000 02236nam a22003258i 4500
001 CR9780511763113
003 UkCbUP
005 20201015164342.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 100506s2011||||enk o ||1 0|eng|d
020 _a9780511763113 (ebook)
020 _z9780521493369 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 4 _aTK5103.485
_b.J36 2011
082 0 0 _a006.3/3
_222
100 1 _aJannach, Dietmar,
_d1973-
_eauthor.
245 1 0 _aRecommender systems :
_ban introduction /
_cDietmar Jannach [and three others].
264 1 _aCambridge :
_bCambridge University Press,
_c2011.
300 _a1 online resource (335 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aIn this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
650 0 _aPersonal communication service systems.
650 0 _aRecommender systems (Information filtering)
776 0 8 _iPrint version:
_z9780521493369
856 4 0 _uhttps://doi.org/10.1017/CBO9780511763113
999 _c121228
_d121228