A graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training

dc.contributor.authorDefazio, Aaron
dc.contributor.authorCaetano, Tiberio
dc.coverage.spatialEdinburgh UK
dc.date.accessioned2015-12-10T23:31:35Z
dc.date.createdJune 26-July 1 2012
dc.date.issued2012
dc.date.updated2016-02-24T08:50:59Z
dc.description.abstractItem neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of a user's rating on an item is then given by that rating of neighbouring items, weighted by their similarity. This paper presents a new neighbourhood approach which we call item fields, whereby an undirected graphical model is formed over the item graph. The resulting prediction rule is a simple generalization of the classical approaches, which takes into account non-local information in the graph, allowing its best results to be obtained when using drastically fewer edges than other neighbourhood approaches. A fast approximate maximum entropy training method based on the Bethe approximation is presented, which uses a simple gradient ascent procedure. When using precomputed sufficient statistics on the Movielens datasets, our method is faster than maximum likelihood approaches by two orders of magnitude.
dc.identifier.isbn9781450312851
dc.identifier.urihttp://hdl.handle.net/1885/68699
dc.publisherConference Organising Committee
dc.relation.ispartofseriesInternational Conference on Machine Learning (ICML 2012)
dc.sourceProceedings of the 29th International Conference on Machine Learning, ICML 2012
dc.subjectKeywords: Bethe approximation; Classical approach; Collaborative filtering; Data sets; Gradient ascent; GraphicaL model; Maximum entropy; Maximum likelihood approaches; Neighbourhood; Nonlocal; Orders of magnitude; Prediction rules; Sufficient statistics; Training
dc.titleA graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training
dc.typeConference paper
local.bibliographicCitation.lastpage272
local.bibliographicCitation.startpage265
local.contributor.affiliationDefazio, Aaron, College of Engineering and Computer Science, ANU
local.contributor.affiliationCaetano, Tiberio, College of Engineering and Computer Science, ANU
local.contributor.authoremailu4406979@anu.edu.au
local.contributor.authoruidDefazio, Aaron, u4406979
local.contributor.authoruidCaetano, Tiberio, u4590840
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080104 - Computer Vision
local.identifier.absfor100503 - Computer Communications Networks
local.identifier.ariespublicationf5625xPUB1802
local.identifier.scopusID2-s2.0-84867114720
local.identifier.uidSubmittedByf5625
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
01_Defazio_A_graphical_model_formulation_2012.pdf
Size:
396.76 KB
Format:
Adobe Portable Document Format
Back to topicon-arrow-up-solid
 
APRU
IARU
 
edX
Group of Eight Member

Acknowledgement of Country

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.


Contact ANUCopyrightDisclaimerPrivacyFreedom of Information

+61 2 6125 5111 The Australian National University, Canberra

TEQSA Provider ID: PRV12002 (Australian University) CRICOS Provider Code: 00120C ABN: 52 234 063 906