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008			230218s2022 sz | s |||| 0|eng d
020			_a9783031188176
024	7		_a10.1007/978-3-031-18817-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aGuan, Weili _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686929
245	1	0	_aGraph Learning for Fashion Compatibility Modeling _cby Weili Guan, Xuemeng Song, Xiaojun Chang, Liqiang Nie
250			_a2nd edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 112 páginas) _b29 ilustraciones, 28 ilustraciones en blanco y negro
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9141162 _aAlgoritmos
700	1		_aSong, Xuemeng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686433
700	1		_aChang, Xiaojun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686930
700	1		_aNie, Liqiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686432
776	0	8	_iPrinted edition: _z9783031188169
776	0	8	_iPrinted edition: _z9783031188183
776	0	8	_iPrinted edition: _z9783031188190
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-18817-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI