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The Resource Probabilistic approaches to recommendations, Nicola Barbieri, Yahoo Labs, Giuseppe Manco, ICAR-CNR, Ettore Ritacco, ICAR-CNR

Probabilistic approaches to recommendations, Nicola Barbieri, Yahoo Labs, Giuseppe Manco, ICAR-CNR, Ettore Ritacco, ICAR-CNR

Label
Probabilistic approaches to recommendations
Title
Probabilistic approaches to recommendations
Statement of responsibility
Nicola Barbieri, Yahoo Labs, Giuseppe Manco, ICAR-CNR, Ettore Ritacco, ICAR-CNR
Creator
Contributor
Author
Subject
Language
eng
Summary
The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively.--
Member of
Assigning source
Provided by Publisher
Cataloging source
YDXCP
http://library.link/vocab/creatorName
Barbieri, Nicola
Dewey number
006.4
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Manco, Giuseppe
  • Ritacco, Ettore
Series statement
Synthesis lectures on data mining and knowledge discovery
Series volume
# 9
http://library.link/vocab/subjectName
  • Recommender systems (Information filtering)
  • Probabilities
Label
Probabilistic approaches to recommendations, Nicola Barbieri, Yahoo Labs, Giuseppe Manco, ICAR-CNR, Ettore Ritacco, ICAR-CNR
Instantiates
Publication
Bibliography note
Includes bibliographical references
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
txt
Content type MARC source
rdacontent.
Contents
Preface / The Recommendation Process / Probabilistic Models for Collaborative Filtering / Bayesian Modeling / Exploiting Probabilistic Models / Contextual Information / Social Recommender Systems / Conclusions / Bibliography / Authors' Biographies
Control code
FIEb17580225
Dimensions
24 cm.
Extent
xv, 181 pages
Isbn
9781627052573
Media category
unmediated
Media MARC source
rdamedia.
Media type code
n
Other physical details
illustrations
System control number
(OCoLC)871788163
Label
Probabilistic approaches to recommendations, Nicola Barbieri, Yahoo Labs, Giuseppe Manco, ICAR-CNR, Ettore Ritacco, ICAR-CNR
Publication
Bibliography note
Includes bibliographical references
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
txt
Content type MARC source
rdacontent.
Contents
Preface / The Recommendation Process / Probabilistic Models for Collaborative Filtering / Bayesian Modeling / Exploiting Probabilistic Models / Contextual Information / Social Recommender Systems / Conclusions / Bibliography / Authors' Biographies
Control code
FIEb17580225
Dimensions
24 cm.
Extent
xv, 181 pages
Isbn
9781627052573
Media category
unmediated
Media MARC source
rdamedia.
Media type code
n
Other physical details
illustrations
System control number
(OCoLC)871788163

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