Recommender Systems and the Social Web Leveraging Tagging Data for Recommender Systems /

There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Gedikli, Fatih (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2013.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03265nam a22004695i 4500
001 978-3-658-01948-8
003 DE-He213
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007 cr nn 008mamaa
008 130330s2013 gw | s |||| 0|eng d
020 |a 9783658019488  |9 978-3-658-01948-8 
024 7 |a 10.1007/978-3-658-01948-8  |2 doi 
040 |d GrThAP 
050 4 |a QA76.9.D343 
072 7 |a UNF  |2 bicssc 
072 7 |a UYQE  |2 bicssc 
072 7 |a COM021030  |2 bisacsh 
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100 1 |a Gedikli, Fatih.  |e author. 
245 1 0 |a Recommender Systems and the Social Web  |h [electronic resource] :  |b Leveraging Tagging Data for Recommender Systems /  |c by Fatih Gedikli. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Vieweg,  |c 2013. 
300 |a XI, 112 p. 29 illus., 14 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Recommender Systems -- Social Tagging -- Algorithms -- Explanations. 
520 |a There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere. Contents -  Recommender Systems -  Social Tagging -  Algorithms -  Explanations   Target Groups ·         Researchers and students of computer science ·         Computer and web programmers   The Author Dr. Fatih Gedikli is a research assistant in computer science at TU Dortmund, Germany. 
650 0 |a Computer science. 
650 0 |a Data mining. 
650 0 |a Information storage and retrieval. 
650 0 |a User interfaces (Computer systems). 
650 1 4 |a Computer Science. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Information Storage and Retrieval. 
650 2 4 |a User Interfaces and Human Computer Interaction. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783658019471 
856 4 0 |u http://dx.doi.org/10.1007/978-3-658-01948-8  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)