Recommender Systems and the Social Web (Record no. 57883)
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| 000 -LEADER | |
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| fixed length control field | 03354nam a22004815i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-658-01948-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421112229.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130330s2013 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783658019488 |
| -- | 978-3-658-01948-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.312 |
| 100 1# - AUTHOR NAME | |
| Author | Gedikli, Fatih. |
| 245 10 - TITLE STATEMENT | |
| Title | Recommender Systems and the Social Web |
| Sub Title | Leveraging Tagging Data for Recommender Systems / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 112 p. 29 illus., 14 illus. in color. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Recommender Systems -- Social Tagging -- Algorithms -- Explanations. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-658-01948-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Wiesbaden : |
| -- | Springer Fachmedien Wiesbaden : |
| -- | Imprint: Springer Vieweg, |
| -- | 2013. |
| 336 ## - | |
| -- | text |
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| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information storage and retrieval. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | User interfaces (Computer systems). |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information Storage and Retrieval. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | User Interfaces and Human Computer Interaction. |
| 912 ## - | |
| -- | ZDB-2-SCS |
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