Probabilistic approaches to recommendations /

Bibliographic Details
Main Authors:	Barbieri, Nicola (Author), Manco, Giuseppe (Author), Ritacco, Ettore (Author)
Format:	eBook
Language:	English
Published:	San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:	Synthesis lectures on data mining and knowledge discovery ; # 9.
Subjects:	Probabilities > Mathematical models. Recommender systems (Information filtering) Bayesian modeling cold start inference influence latent factor models learning matrix factorization maximum likelihood mixture models prediction probability recommender systems social contagion social networks topic modeling Electronic books.
Online Access:	Connect to the full text of this electronic book

Description
Abstract:	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.
Item Description:	Series from website.
Physical Description:	1 online resource (xv, 181 pages) : illustrations. Also available in print.
Format:	Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader.
Bibliography:	Includes bibliographical references (pages 161-179).
ISBN:	9781627052580
ISSN:	2151-0075 ;
DOI:	10.2200/S00574ED1V01Y201403DMK009
Access:	Abstract freely available; full-text restricted to subscribers or individual document purchasers.