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008			170130s2016 gw | s |||| 0|eng d
020			_a9783319413570
024	7		_a10.1007/978-3-319-41357-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .I58 _b2016 EB
100	1		_aSymeonidis, Panagiotis _9101570
245	1	0	_aMatrix and Tensor Factorization Techniques for Recommender Systems _cby Panagiotis Symeonidis, Andreas Zioupos
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (VI, 102 páginas) _b51 ilustraciones, 22 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSpringerBriefs in Computer Science _x2191-5768
505	0		_aPart I Matrix Factorization Techniques -- 1. Introduction -- 2. Related Work on Matrix Factorization -- 3. Performing SVD on matrices and its Extensions -- 4. Experimental Evaluation on Matrix Decomposition Methods -- Part II Tensor Factorization Techniques -- 5. Related Work on Tensor Factorization -- 6. HOSVD on Tensors and its Extensions -- 7. Experimental Evaluation on Tensor Decomposition Methods -- 8 Conclusions and Future Work.
520			_aThis book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods. It also contains two chapters, where different matrix and tensor methods are compared experimentally on real data sets, such as Epinions, GeoSocialRec, Last.fm, BibSonomy, etc. and provides further insights into the advantages and disadvantages of each method. The book offers a rich blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, recommender systems and dimensionality reduction methods.
988			_aEBOOK, EBSPRINGER
650	0	7	_aSistemas interactivos (Informática) _2embne _9139854
700	1		_aZioupos, Andreas _0Local _eautor _9101571
830		0	_aSpringerBriefs in Computer Science _x2191-5768 _0http://id.loc.gov/authorities/names/no2011109396 _9134081
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-41357-0zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12981230 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11603070 _z06-04-17
998			_b06/2020 _dz _eu _feng _ggw _h0