Название: Session-Based Recommender Systems Using Deep LearningАвтор: Reza Ravanmehr, Rezvan Mohamadrezaei
Издательство: Springer
Год: 2024
Страниц: 314
Язык: английский
Формат: pdf (true)
Размер: 28.9 MB
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.
Among the various Machine Learning algorithms, Deep Learning has recently been dramatically used in different scopes. Deep Learning models have been significantly employed in effectively extracting hidden patterns from vast amounts of data and modeling interdependent variables to solve complex problems. Since this book aims to discuss the session-based recommender system approaches using Deep Learning models, brief explanations of various deep neural networks are provided in the Chapter 1. For this purpose, the history, basic concepts, advantages/applications, and fundamental models of Deep Learning are discussed.
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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