Matrix and tensor factorization techniques for recommender systems

• 作者： Symeonidis, Panagiotis, author.
• 其他作者：
  • Zioupos, Andreas, author.
  • SpringerLink (Online service)
• 其他題名：
  • SpringerBriefs in computer science.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： SpringerBriefs in computer science,
• 主題： Recommender systems (Information filtering) , Computer Science. , Information Storage and Retrieval. , Mathematical Applications in Computer Science. , Mathematics of Computing. , Artificial Intelligence (incl. Robotics)
• ISBN： 9783319413570 (electronic bk.) 、 9783319413563 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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.
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• 摘要註: 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.
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