04082nam a22005535i 4500 978-3-319-41357-0 DE-He213 20210618080927.0 cr nn 008mamaa 170130s2016 gw | s |||| 0|eng d 9783319413570 978-3-319-41357-0 10.1007/978-3-319-41357-0 doi QA75.5-76.95 UNH bicssc COM030000 bisacsh UNH thema UND thema 025.04 23 Symeonidis, Panagiotis. author. aut http://id.loc.gov/vocabulary/relators/aut Matrix and Tensor Factorization Techniques for Recommender Systems [electronic resource] / by Panagiotis Symeonidis, Andreas Zioupos. 1st ed. 2016. Cham : Springer International Publishing : Imprint: Springer, 2016. VI, 102 p. 51 illus., 22 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computer Science, 2191-5768 Part 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. This 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. Information storage and retrieval. Computer science—Mathematics. Computer mathematics. Artificial intelligence. Information Storage and Retrieval. https://scigraph.springernature.com/ontologies/product-market-codes/I18032 Mathematical Applications in Computer Science. https://scigraph.springernature.com/ontologies/product-market-codes/M13110 Mathematics of Computing. https://scigraph.springernature.com/ontologies/product-market-codes/I17001 Artificial Intelligence. https://scigraph.springernature.com/ontologies/product-market-codes/I21000 Zioupos, Andreas. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) Springer Nature eBook Printed edition: 9783319413563 Printed edition: 9783319413587 SpringerBriefs in Computer Science, 2191-5768 https://doi.org/10.1007/978-3-319-41357-0 ZDB-2-SCS ZDB-2-SXCS Computer Science (SpringerNature-11645) Computer Science (R0) (SpringerNature-43710)