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Overview: 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. 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.
Genre: Non-Fiction > Tech & Devices
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