000 03613nam a22005415i 4500
001 978-3-319-41357-0
003 DE-He213
005 20200421112226.0
007 cr nn 008mamaa
008 170130s2016 gw | s |||| 0|eng d
020 _a9783319413570
_9978-3-319-41357-0
024 7 _a10.1007/978-3-319-41357-0
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aSymeonidis, Panagiotis.
_eauthor.
245 1 0 _aMatrix and Tensor Factorization Techniques for Recommender Systems
_h[electronic resource] /
_cby Panagiotis Symeonidis, Andreas Zioupos.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aVI, 102 p. 51 illus., 22 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aInformation storage and retrieval.
650 0 _aArtificial intelligence.
650 0 _aComputer mathematics.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aMathematical Applications in Computer Science.
650 2 4 _aMathematics of Computing.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aZioupos, Andreas.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319413563
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-41357-0
912 _aZDB-2-SCS
942 _cEBK
999 _c57713
_d57713