Coverart for item
The Resource Probabilistic approaches to recommendations, Nicola Barbieri, Giuseppe Manco, Ettore Ritacco

Probabilistic approaches to recommendations, Nicola Barbieri, Giuseppe Manco, Ettore Ritacco

Label
Probabilistic approaches to recommendations
Title
Probabilistic approaches to recommendations
Statement of responsibility
Nicola Barbieri, Giuseppe Manco, Ettore Ritacco
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
Member of
Cataloging source
NhCcYBP
http://library.link/vocab/creatorName
Barbieri, Nicola
Dewey number
001.64
Illustrations
illustrations
Index
no index present
LC call number
QA76.9.I58
LC item number
B276 2014
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Manco, Giuseppe
  • Ritacco, Ettore
  • ProQuest (Firm)
Series statement
Synthesis lectures on data mining and knowledge discovery,
Series volume
#9
http://library.link/vocab/subjectName
  • Recommender systems (Information filtering)
  • Probabilities
Target audience
  • adult
  • specialized
Label
Probabilistic approaches to recommendations, Nicola Barbieri, Giuseppe Manco, Ettore Ritacco
Instantiates
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 161-179)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Evaluation
  • Network-Aware Topic Models
  • 6.2.2.
  • Social Probabilistic Matrix Factorization
  • 6.2.3.
  • Stochastic Block Models for Social Rating Networks
  • 6.3.
  • Influence in Social Networks
  • 6.3.1.
  • Identifying Social Influence
  • 6.3.2.
  • 1.2.2.
  • Influence Maximization and Viral Marketing
  • 6.3.3.
  • Exploiting Influence in Recommender Systems
  • 7.
  • Conclusions
  • 7.1.
  • Application-Specific Challenges
  • 7.2.
  • Technological Challenges
  • A.
  • Main Challenges
  • Parameter Estimation and Inference
  • A.1.
  • Expectation Maximization Algorithm
  • A.2.
  • Variational Inference
  • A.3.
  • Gibbs Sampling
  • 1.3.
  • Recommendation as information filtering
  • 1.3.1.
  • Demographic Filtering
  • 1.3.2.
  • Content-Based Filtering
  • 1.4.
  • Machine generated contents note:
  • Collaborative Filtering
  • 1.4.1.
  • Neighborhood-Based Approaches
  • 1.4.2.
  • Latent Factor Models
  • 1.4.3.
  • Baseline Models and Collaborative Filtering
  • 2.
  • Probabilistic Models for Collaborative Filtering
  • 2.1.
  • 1.
  • Predictive Modeling
  • 2.2.
  • Mixture Membership Models
  • 2.2.1.
  • Mixtures and Predictive Modeling
  • 2.2.2.
  • Model-Based Co-Clustering
  • 2.3.
  • Probabilistic Latent Semantic Models
  • 2.3.1.
  • Recommendation Process
  • Probabilistic Latent Semantic Analysis
  • 2.3.2.
  • Probabilistic Matrix Factorization
  • 2.4.
  • Summary
  • 3.
  • Bayesian Modeling
  • 3.1.
  • Bayesian Regularization and Model Selection
  • 3.2.
  • 1.1.
  • Latent Dirichlet Allocation
  • 3.2.1.
  • Inference and Parameter Estimation
  • 3.2.2.
  • Bayesian Topic Models for Recommendation
  • 3.3.
  • Bayesian Co-Clustering
  • 3.3.1.
  • Hierarchical Models
  • 3.4.
  • Introduction
  • Bayesian Matrix Factorization
  • 3.5.
  • Summary
  • 4.
  • Exploiting Probabilistic Models
  • 4.1.
  • Probabilistic Modeling and Decision Theory
  • 4.1.1.
  • Minimizing the Prediction Error
  • 4.1.2.
  • 1.2.
  • Recommendation Accuracy
  • 4.2.
  • Beyond Prediction Accuracy
  • 4.2.1.
  • Data Analysis with Topic Models
  • 4.2.2.
  • Pattern Discovery Using Co-Clusters
  • 4.2.3.
  • Diversification with Topic Models
  • 5.
  • Formal Framework
  • Contextual Information
  • 5.1.
  • Integrating Content Features
  • 5.1.1.
  • Cold-Start Problem
  • 5.1.2.
  • Modeling Text and Preferences
  • 5.2.
  • Sequential Modeling
  • 5.2.1.
  • 1.2.1.
  • Markov Models
  • 5.2.2.
  • Probabilistic Tensor Factorization
  • 6.
  • Social Recommender Systems
  • 6.1.
  • Modeling Social Rating Networks
  • 6.2.
  • Probabilistic Approaches for Social Rating Networks
  • 6.2.1.
Control code
MSTDDA1707036
Dimensions
unknown
Extent
1 online resource ( xv, 181 pages ):
File format
multiple file formats
Form of item
online
Isbn
9781627052580
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.2200/S00574ED1V01Y201403DMK009
Other physical details
illustrations.
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000452
Reformatting quality
access
Reproduction note
Electronic reproduction.
Specific material designation
remote
Label
Probabilistic approaches to recommendations, Nicola Barbieri, Giuseppe Manco, Ettore Ritacco
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 161-179)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Evaluation
  • Network-Aware Topic Models
  • 6.2.2.
  • Social Probabilistic Matrix Factorization
  • 6.2.3.
  • Stochastic Block Models for Social Rating Networks
  • 6.3.
  • Influence in Social Networks
  • 6.3.1.
  • Identifying Social Influence
  • 6.3.2.
  • 1.2.2.
  • Influence Maximization and Viral Marketing
  • 6.3.3.
  • Exploiting Influence in Recommender Systems
  • 7.
  • Conclusions
  • 7.1.
  • Application-Specific Challenges
  • 7.2.
  • Technological Challenges
  • A.
  • Main Challenges
  • Parameter Estimation and Inference
  • A.1.
  • Expectation Maximization Algorithm
  • A.2.
  • Variational Inference
  • A.3.
  • Gibbs Sampling
  • 1.3.
  • Recommendation as information filtering
  • 1.3.1.
  • Demographic Filtering
  • 1.3.2.
  • Content-Based Filtering
  • 1.4.
  • Machine generated contents note:
  • Collaborative Filtering
  • 1.4.1.
  • Neighborhood-Based Approaches
  • 1.4.2.
  • Latent Factor Models
  • 1.4.3.
  • Baseline Models and Collaborative Filtering
  • 2.
  • Probabilistic Models for Collaborative Filtering
  • 2.1.
  • 1.
  • Predictive Modeling
  • 2.2.
  • Mixture Membership Models
  • 2.2.1.
  • Mixtures and Predictive Modeling
  • 2.2.2.
  • Model-Based Co-Clustering
  • 2.3.
  • Probabilistic Latent Semantic Models
  • 2.3.1.
  • Recommendation Process
  • Probabilistic Latent Semantic Analysis
  • 2.3.2.
  • Probabilistic Matrix Factorization
  • 2.4.
  • Summary
  • 3.
  • Bayesian Modeling
  • 3.1.
  • Bayesian Regularization and Model Selection
  • 3.2.
  • 1.1.
  • Latent Dirichlet Allocation
  • 3.2.1.
  • Inference and Parameter Estimation
  • 3.2.2.
  • Bayesian Topic Models for Recommendation
  • 3.3.
  • Bayesian Co-Clustering
  • 3.3.1.
  • Hierarchical Models
  • 3.4.
  • Introduction
  • Bayesian Matrix Factorization
  • 3.5.
  • Summary
  • 4.
  • Exploiting Probabilistic Models
  • 4.1.
  • Probabilistic Modeling and Decision Theory
  • 4.1.1.
  • Minimizing the Prediction Error
  • 4.1.2.
  • 1.2.
  • Recommendation Accuracy
  • 4.2.
  • Beyond Prediction Accuracy
  • 4.2.1.
  • Data Analysis with Topic Models
  • 4.2.2.
  • Pattern Discovery Using Co-Clusters
  • 4.2.3.
  • Diversification with Topic Models
  • 5.
  • Formal Framework
  • Contextual Information
  • 5.1.
  • Integrating Content Features
  • 5.1.1.
  • Cold-Start Problem
  • 5.1.2.
  • Modeling Text and Preferences
  • 5.2.
  • Sequential Modeling
  • 5.2.1.
  • 1.2.1.
  • Markov Models
  • 5.2.2.
  • Probabilistic Tensor Factorization
  • 6.
  • Social Recommender Systems
  • 6.1.
  • Modeling Social Rating Networks
  • 6.2.
  • Probabilistic Approaches for Social Rating Networks
  • 6.2.1.
Control code
MSTDDA1707036
Dimensions
unknown
Extent
1 online resource ( xv, 181 pages ):
File format
multiple file formats
Form of item
online
Isbn
9781627052580
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.2200/S00574ED1V01Y201403DMK009
Other physical details
illustrations.
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000452
Reformatting quality
access
Reproduction note
Electronic reproduction.
Specific material designation
remote

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      400 West 14th Street, Rolla, MO, 65409, US
      37.955220 -91.772210
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