04008nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001600162072002300178072001500201100007800216245012500294250001800419264006900437300006500506336002600571337002600597338003600623347002400659505031900683520197101002650003202973650004703005650002903052650002703081650003203108650003903140650002903179650003203208700008403240710003403324773002603358776003603384776003603420776003603456856004603492978-3-031-42559-2DE-He21320251030110857.0cr nn 008mamaa231220s2024    sz |    s    |||| 0|eng d  a97830314255927 a10.1007/978-3-031-42559-22doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aRavanmehr, Reza.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aSession-Based Recommender Systems Using Deep Learningh[electronic resource] /cby Reza Ravanmehr, Rezvan Mohamadrezaei.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIX, 301 p. 99 illus., 71 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a1. 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.  aThis 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 3analyses 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. 0aComputational intelligence. 0aInformation storage and retrieval systems. 0aArtificial intelligence. 0aQuantitative research.14aComputational Intelligence.24aInformation Storage and Retrieval.24aArtificial Intelligence.24aData Analysis and Big Data.1 aMohamadrezaei, Rezvan.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303142558508iPrinted edition:z978303142560808iPrinted edition:z978303142561540uhttps://doi.org/10.1007/978-3-031-42559-2