Learning sparse heterogeneous user preferences

Markus Peters; Wolfgang Ketter

Learning sparse heterogeneous user preferences

Department of Technology and Operations Management

Research output: Contribution to conference › Paper › Academic

Abstract

Models of user preferences will be at the core of the next generation of personalized Infor- mation Systems. We propose HPREF, an algorithm for learning a hierarchical, probabilistic preference model that integrates sparse preferences from multiple like-minded users in a principled fashion. Our preliminary experiments indicate that HPREF outperforms previous preference learning approaches and suggest several directions for further improvement.

Original language	English
Pages	223-228
Number of pages	6
Publication status	Published - 2012
Event	22nd Workshop on Information Technologies and Systems, WITS 2012 - Orlando, FL, United States Duration: 15 Dec 2012 → 16 Dec 2012

Conference

Conference	22nd Workshop on Information Technologies and Systems, WITS 2012
Country/Territory	United States
City	Orlando, FL
Period	15/12/12 → 16/12/12

Cite this

@conference{0b525e1b44f94e14bbe85e6979aeb0b0,

title = "Learning sparse heterogeneous user preferences",

abstract = "Models of user preferences will be at the core of the next generation of personalized Infor- mation Systems. We propose HPREF, an algorithm for learning a hierarchical, probabilistic preference model that integrates sparse preferences from multiple like-minded users in a principled fashion. Our preliminary experiments indicate that HPREF outperforms previous preference learning approaches and suggest several directions for further improvement.",

author = "Markus Peters and Wolfgang Ketter",

year = "2012",

language = "English",

pages = "223--228",

note = "22nd Workshop on Information Technologies and Systems, WITS 2012 ; Conference date: 15-12-2012 Through 16-12-2012",

}

TY - CONF

T1 - Learning sparse heterogeneous user preferences

AU - Peters, Markus

AU - Ketter, Wolfgang

PY - 2012

Y1 - 2012

N2 - Models of user preferences will be at the core of the next generation of personalized Infor- mation Systems. We propose HPREF, an algorithm for learning a hierarchical, probabilistic preference model that integrates sparse preferences from multiple like-minded users in a principled fashion. Our preliminary experiments indicate that HPREF outperforms previous preference learning approaches and suggest several directions for further improvement.

AB - Models of user preferences will be at the core of the next generation of personalized Infor- mation Systems. We propose HPREF, an algorithm for learning a hierarchical, probabilistic preference model that integrates sparse preferences from multiple like-minded users in a principled fashion. Our preliminary experiments indicate that HPREF outperforms previous preference learning approaches and suggest several directions for further improvement.

UR - http://www.scopus.com/inward/record.url?scp=84902435979&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:84902435979

SP - 223

EP - 228

T2 - 22nd Workshop on Information Technologies and Systems, WITS 2012

Y2 - 15 December 2012 through 16 December 2012

ER -

Learning sparse heterogeneous user preferences

Abstract

Conference

Other files and links

Fingerprint

Cite this