Panorama of Deep Learning Based Recommender System - Sinha, Bam Bahadur

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Résumé :
In recent years, there has been an unprecedented growth in research publications on methods for profound learners, which demonstrate the unavoidable generality of deep learning while proposing any recommender system. The structure of the book demonstrates the impact of deep learning on recommender systems. The chapters of the book can be assembled into two categories:The omnipresence of deep learning in specific domains of recommender system: These chapters address deep learning techniques in recommender systems, including recommendation with deep learning techniques in content-based systems (Chapter 3), recommendation with deep learning techniques in collaborative systems (Chapter 4), recommendation with deep learning techniques in the hybrid system (Chapter 5), recommendation in context-aware systems (Chapter 6), and combination of social network & trust-aware recommender system with deep learning (Chapter 7). Advancement and application of deep recommender system: Chapter 8 is primarily aimed at providing the readers with basic ideas and principles driving trends in recent years. Although all the recent innovations cannot be addressed in depth in a single book, the content in the closing chapter of the book is intended to perform the role of ice-breaking in advanced topics. The chapter further discusses other application scenarios using recommendations technologies, such as news recommendation, and computational advertising. Since this book is ostensibly written as a textbook, it is understood that a significant part of the audience will be industry experts and scholars. Thus, effort has been made to compose the book content in such a way that it is always valuable from an applicable and research point of view. For more details, please visit https://centralwestpublishing.com...

Sommaire:
In recent years, there has been an unprecedented growth in research publications on methods for profound learners, which demonstrate the unavoidable generality of deep learning while proposing any recommender system. The structure of the book demonstrates the impact of deep learning on recommender systems. The chapters of the book can be assembled into two categories:The omnipresence of deep learning in specific domains of recommender system: These chapters address deep learning techniques in recommender systems, including recommendation with deep learning techniques in content-based systems (Chapter 3), recommendation with deep learning techniques in collaborative systems (Chapter 4), recommendation with deep learning techniques in the hybrid system (Chapter 5), recommendation in context-aware systems (Chapter 6), and combination of social network & trust-aware recommender system with deep learning (Chapter 7). Advancement and application of deep recommender system: Chapter 8 is primarily aimed at providing the readers with basic ideas and principles driving trends in recent years. Although all the recent innovations cannot be addressed in depth in a single book, the content in the closing chapter of the book is intended to perform the role of ice-breaking in advanced topics. The chapter further discusses other application scenarios using recommendations technologies, such as news recommendation, and computational advertising. Since this book is ostensibly written as a textbook, it is understood that a significant part of the audience will be industry experts and scholars. Thus, effort has been made to compose the book content in such a way that it is always valuable from an applicable and research point of view. For more details, please visit https://centralwestpublishing.com...