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Session-based recommender systems using deep learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Session-based recommender systems using deep learningby Reza Ravanmehr, Rezvan Mohamadrezaei.
作者:	Ravanmehr, Reza.
其他作者:	Mohamadrezaei, Rezvan.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xix, 301 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Recommender systems (Information filtering)
電子資源:	https://doi.org/10.1007/978-3-031-42559-2
ISBN:	9783031425592$q(electronic bk.)

Session-based recommender systems using deep learning
Ravanmehr, Reza.

Session-based recommender systems using deep learning[electronic resource] /by Reza Ravanmehr, Rezvan Mohamadrezaei. - Cham :Springer Nature Switzerland :2024. - xix, 301 p. :ill. (some col.), digital ;24 cm.

1. Introduction to Session-Based Recommender Systems -- 2. Deep Learning Overview -- 3. Deep Discriminative Session-Based Recommender Systems -- 4. Deep Generative Session-Based Recommender Systems -- 5. Hybrid/Advanced Session-Based Recommender Systems -- 6. Learning to Rank in Session-Based Recommender Systems.

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.

ISBN: 9783031425592$q(electronic bk.)

Standard No.: 10.1007/978-3-031-42559-2doiSubjects--Topical Terms:

310886
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 025.04

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520 $a 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.
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