Matrix and Tensor Factorization Techniques for Recommender Systems

• Κύριοι συγγραφείς: Symeonidis, Panagiotis (Συγγραφέας), Zioupos, Andreas (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: SpringerBriefs in Computer Science,
• Θέματα: Computer science.; Computer science > Mathematics.; Information storage and retrieval.; Artificial intelligence.; Computer mathematics.; Computer Science.; Information Storage and Retrieval.; Mathematical Applications in Computer Science.; Mathematics of Computing.; Artificial Intelligence (incl. Robotics).

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 Factoriz...

Πίνακας περιεχομένων:

• 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.