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020 _a9783031425592
_9978-3-031-42559-2
024 7 _a10.1007/978-3-031-42559-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
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100 1 _aRavanmehr, Reza.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_968410
245 1 0 _aSession-Based Recommender Systems Using Deep Learning
_h[electronic resource] /
_cby Reza Ravanmehr, Rezvan Mohamadrezaei.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIX, 301 p. 99 illus., 71 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_964052
650 0 _aInformation storage and retrieval systems.
_965568
650 0 _aArtificial intelligence.
_958441
650 0 _aQuantitative research.
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aInformation Storage and Retrieval.
_965570
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aData Analysis and Big Data.
700 1 _aMohamadrezaei, Rezvan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_968411
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031425585
776 0 8 _iPrinted edition:
_z9783031425608
776 0 8 _iPrinted edition:
_z9783031425615
856 4 0 _uhttps://doi.org/10.1007/978-3-031-42559-2
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c56867
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