Probabilistic approaches to recommendations

Resource Information

The work Probabilistic approaches to recommendations represents a distinct intellectual or artistic creation found in Missouri University of Science & Technology Library. This resource is a combination of several types including: Work, Language Material, Books.

Label

Probabilistic approaches to recommendations

Statement of responsibility

Nicola Barbieri, Giuseppe Manco, Ettore Ritacco

Creator

• Barbieri, Nicola, (Computer scientist)

Contributor

• Manco, Giuseppe
• ProQuest (Firm)
• Ritacco, Ettore

Author

• Ritacco, Ettore
• Barbieri, Nicola, (Computer scientist)
• Manco, Giuseppe

Subject

• inference
• influence
• latent factor models
• learning
• matrix factorization
• maximum likelihood
• mixture models
• prediction
• probability
• recommender systems
• social contagion
• social networks
• topic modeling
• Bayesian modeling
• Probabilities
• Recommender systems (Information filtering) -- Statistical methods
• cold start

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

• Synthesis lectures on data mining and knowledge discovery, #9.

Cataloging source

NhCcYBP

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

Series statement

Synthesis lectures on data mining and knowledge discovery,

Series volume

#9

Target audience

• adult
• specialized