Session-Based Recommender Systems Using Deep Learning (eBook)
XIX, 301 Seiten
Springer Nature Switzerland (Verlag)
978-3-031-42559-2 (ISBN)
This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied.
The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary.
This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.Reza Ravanmehr has been a faculty member of the Department of Computer Engineering at Central Tehran Branch, Islamic Azad University, since 2001. His main research interests are recommender systems, large-scale data management systems, and social network analysis. He has published over 60 scientific papers, mainly in social network analysis and recommender systems.
Rezvan Mohamadrezaei is currently a Ph.D. candidate in software systems at Central Tehran Branch, Islamic Azad University. Her current research interests are in the areas of deep learning, recommender systems, and information retrieval. She has been a faculty member of the Computer Engineering Department at Karoon Institute of Higher Education, Ahvaz, since 2013.
Erscheint lt. Verlag | 20.12.2023 |
---|---|
Zusatzinfo | XIX, 301 p. 99 illus., 71 illus. in color. |
Sprache | englisch |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Mathematik / Informatik ► Mathematik ► Statistik | |
Technik | |
Schlagworte | Big data analysis • Deep learning • machine learning • Recommender Systems • social network analysis |
ISBN-10 | 3-031-42559-6 / 3031425596 |
ISBN-13 | 978-3-031-42559-2 / 9783031425592 |
Haben Sie eine Frage zum Produkt? |
Größe: 29,7 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich