One-class collaborative filtering with random graphs

Ulrich Paquet, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the odds of a user disliking an item from simply being unaware of it. The latent signal is treated as an unobserved random graph connecting users with items they might have encountered. We demonstrate how large-scale distributed learning can be achieved through a combination of stochastic gradient descent and mean field variational inference over random graph samples. A fine-grained comparison is done against a state of the art baseline on real world data. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish
Title of host publicationWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages999-1008
Number of pages10
ISBN (Print)9781450320351
DOIs
StatePublished - 2013
Externally publishedYes
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: 13 May 201317 May 2013

Publication series

NameWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Conference

Conference22nd International Conference on World Wide Web, WWW 2013
Country/TerritoryBrazil
CityRio de Janeiro
Period13/05/1317/05/13

Keywords

  • One-class collaborative filtering
  • Random graph
  • Variational inference

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