Session-Based Recommender Systems Using Deep Learning

ISBN-13: 9783031425585 / Angielski

Reza Ravanmehr; Rezvan Mohamadrezaei

Spis treści

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.

O autorze

Reza Ravanmehr has been a faculty member of the Department of Computer Engineering at Central Tehran Branch, Islamic Azad University, since 2001. His main research interests are recommender systems, large-scale data management systems, and social network analysis. He has published over 60 scientific papers, mainly in social network analysis and recommender systems.

Rezvan Mohamadrezaei is currently a Ph.D. candidate in software systems at Central Tehran Branch, Islamic Azad University. Her current research interests are in the areas of deep learning, recommender systems, and information retrieval. She has been a faculty member of the Computer Engineering Department at Karoon Institute of Higher Education, Ahvaz, since 2013.

Blurb

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.