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Probabilistic Latent Maximal Marginal Relevance

dc.contributor.authorGuo, Shengbo
dc.contributor.authorSanner, Scott
dc.coverage.spatialGeneva Switzerland
dc.date.accessioned2015-12-08T22:22:01Z
dc.date.createdJuly 19 2010
dc.identifier.isbn9781450301534
dc.identifier.urihttp://hdl.handle.net/1885/32382
dc.publisherAssociation for Computing Machinery Inc (ACM)
dc.relation.ispartofseriesInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010)
dc.sourceProceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010)
dc.subjectKeywords: Diversity; Diversity metrics; GraphicaL model; Latent semantic indexing; Latent variable; Maximal Marginal Relevance; Objective criteria; Probabilistic inference; Recommender systems; Similarity metrics; Term Frequency; Graphic methods; Information retrie Diversity; Graphical models; Maximal Marginal Relevance
dc.titleProbabilistic Latent Maximal Marginal Relevance
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2010
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.ariespublicationu4963866xPUB91
local.type.statusPublished Version
local.contributor.affiliationGuo, Shengbo, College of Engineering and Computer Science, ANU
local.contributor.affiliationSanner, Scott, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage2
local.identifier.doi10.1145/1835449.1835639
local.identifier.absseo890299 - Computer Software and Services not elsewhere classified
dc.date.updated2016-06-14T09:10:12Z
local.identifier.scopusID2-s2.0-77956042078
CollectionsANU Research Publications

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