Session-Based Recommender Systems Using Deep Learning

Name: Session-Based Recommender Systems Using Deep Learning
Brand: Springer, Berlin
SKU: 9783031425585
Price: 64921 HUF
Availability: InStock

Szerző: Reza Ravanmehr, Rezvan Mohamadrezaei

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer, Berlin

Elérhetőség: Beszállítói készleten

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This book focuses on the widespread use of deep neural networks and their various techniques in sess...

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Szerző

Reza Ravanmehr, Rezvan Mohamadrezaei

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2024

oldal

280

EAN

9783031425585

Enbook ID

43830186

Kiadó

Springer, Berlin

Súly

630

Méretek

155 x 235

Teljes leírás

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.

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Session-Based Recommender Systems Using Deep Learning

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