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020 _a9783031188176
_9978-3-031-18817-6
024 7 _a10.1007/978-3-031-18817-6
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
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072 7 _aUND
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072 7 _aCOM030000
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072 7 _aUND
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082 0 4 _a025.04
_223
100 1 _aGuan, Weili.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988265
245 1 0 _aGraph Learning for Fashion Compatibility Modeling
_h[electronic resource] /
_cby Weili Guan, Xuemeng Song, Xiaojun Chang, Liqiang Nie.
250 _a2nd ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXIV, 112 p. 29 illus., 28 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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_2rdacarrier
347 _atext file
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490 1 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
505 0 _aIntroduction -- Correlation-oriented Graph Learning for OCM -- Modality-oriented Graph Learning for OCM -- Unsupervised Disentangled Graph Learning for OCM -- Supervised Disentangled Graph Learning for OCM -- Heterogeneous Graph Learning for Personalized OCM -- Research Frontiers.
520 _aThis book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book gives comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.
650 0 _aInformation storage and retrieval systems.
_922213
650 0 _aBuilding information modeling.
_98102
650 0 _aApplication software.
_988267
650 0 _aData mining.
_93907
650 1 4 _aInformation Storage and Retrieval.
_923927
650 2 4 _aBuilding Information Modeling.
_98102
650 2 4 _aComputer and Information Systems Applications.
_988270
650 2 4 _aData Mining and Knowledge Discovery.
_988271
700 1 _aSong, Xuemeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988272
700 1 _aChang, Xiaojun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988274
700 1 _aNie, Liqiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_988275
710 2 _aSpringerLink (Online service)
_988278
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031188169
776 0 8 _iPrinted edition:
_z9783031188183
776 0 8 _iPrinted edition:
_z9783031188190
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_988279
856 4 0 _uhttps://doi.org/10.1007/978-3-031-18817-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c86226
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