Session-Based Recommender Systems Using Deep Learning

<p>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.</p><p>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.</p>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. <p></p><br><p></p>