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GroupBox: A generative model for group recommendation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CVAP)ORCID iD: 0000-0002-8640-9370
Microsoft R&D.
Microsoft Research .
Microsoft Research .
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2015 (English)Report (Refereed)
Abstract [en]

In this paper, we present a principled probabilistic framework – GroupBox – for making recommendations to groups. GroupBox is able to model user influence within a group, the suitability of an item to a group context, and the differences in user preference between individual and group contexts. Efficient scalable inference algorithms are used for GroupBox, which makes it applicable to large-scale datasets. We run experiments on a large-scale TV viewing dataset collected by Nielsen and show how the model can be used to understand both context and influence. The experimental results on the large scale real data provide a deep understanding of the individual behaviours in group context.

Place, publisher, year, edition, pages
Microsoft Research , 2015. , 8 p.
Series
TechReport, MSR-TR–2015-61
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-171967OAI: oai:DiVA.org:kth-171967DiVA: diva2:844996
Note

QC 20150811

Available from: 2015-08-10 Created: 2015-08-10 Last updated: 2015-08-11Bibliographically approved

Open Access in DiVA

fulltext(27565 kB)223 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf