Recommender systems : an introduction / Dietmar Jannach [and three others].
Jannach, Dietmar, 1973-| Call Number | 006.3/3 |
| Author | Jannach, Dietmar, 1973- author. |
| Title | Recommender systems : an introduction / Dietmar Jannach [and three others]. |
| Physical Description | 1 online resource (335 pages) : digital, PDF file(s). |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Summary | In 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. |
| Subject | Personal communication service systems. Recommender systems (Information filtering) |
| Multimedia |
Total Ratings:
0
02314nam a22003498i 4500
001
vtls001585350
003
VRT
005
20200921122600.0
006
m|||||o||d||||||||
007
cr||||||||||||
008
200921s2011||||enk o ||1 0|eng|d
020
$a 9780511763113 (ebook)
020
$z 9780521493369 (hardback)
035
$a (UkCbUP)CR9780511763113
039
9
$y 202009211226 $z santha
040
$a UkCbUP $b eng $e rda $c UkCbUP
050
0
4
$a TK5103.485 $b .J36 2011
082
0
0
$a 006.3/3 $2 22
100
1
$a Jannach, Dietmar, $d 1973- $e author.
245
1
0
$a Recommender systems : $b an introduction / $c Dietmar Jannach [and three others].
264
1
$a Cambridge : $b Cambridge University Press, $c 2011.
300
$a 1 online resource (335 pages) : $b digital, PDF file(s).
336
$a text $b txt $2 rdacontent
337
$a computer $b c $2 rdamedia
338
$a online resource $b cr $2 rdacarrier
500
$a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520
$a In 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
$a Personal communication service systems.
650
0
$a Recommender systems (Information filtering)
776
0
8
$i Print version: $z 9780521493369
856
4
0
$u https://doi.org/10.1017/CBO9780511763113
999
$a VIRTUA
No Reviews to Display
| Summary | In 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. |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Subject | Personal communication service systems. Recommender systems (Information filtering) |
| Multimedia |