LEADER 05757nam 2200469 450 001 9910799492003321 005 20240119114056.0 010 $a3-031-42559-6 035 $a(CKB)29449608500041 035 $a(MiAaPQ)EBC31051225 035 $a(Au-PeEL)EBL31051225 035 $a(EXLCZ)9929449608500041 100 $a20240119d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSession-Based Recommender Systems Using Deep Learning /$fReza Ravanmehr and Rezvan Mohamadrezaei 205 $aFirst edition. 210 1$aCham, Switzerland :$cSpringer,$d[2024] 210 4$dİ2024 215 $a1 online resource (314 pages) 311 08$a9783031425585 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Aims and Scope -- Main Emphasis -- Target Audience -- Prerequisites -- Short Summary -- Acknowledgements -- Contents -- About the Authors -- Chapter 1: Introduction to Session-Based Recommender Systems -- 1.1 Introduction -- 1.2 Recommender Systems -- 1.3 Fundamentals of Session-Based Recommender Systems -- 1.3.1 Basic Concepts of SBRS -- 1.3.2 Challenges of SBRS -- 1.3.3 Session-Based vs. Sequential vs. Session-Aware Recommender Systems -- 1.4 Session-Based Recommender System Approaches -- 1.4.1 Traditional SBRS -- 1.4.1.1 Pattern/Rule Mining -- 1.4.1.2 K-Nearest Neighbors -- 1.4.1.3 Markov Chain -- 1.4.1.4 Generative Probabilistic Model -- 1.4.1.5 Latent Representation -- 1.4.2 Deep Learning SBRS -- 1.5 Conclusion -- References -- Chapter 2: Deep Learning Overview -- 2.1 Introduction -- 2.2 Fundamentals of Deep Learning -- 2.2.1 History of Deep Learning -- 2.2.2 AI, ML, and DL -- 2.2.3 Advantages of Deep Learning -- 2.2.4 General Process of Deep Learning-Based Solutions -- 2.2.5 Taxonomy of Deep Learning Models -- 2.3 Deep Discriminative Models -- 2.3.1 Multilayer Perceptron -- 2.3.2 Convolutional Neural Network -- 2.3.3 Recurrent Neural Network -- 2.3.3.1 LSTM -- 2.3.3.2 GRU -- 2.4 Deep Generative Models -- 2.4.1 Autoencoders -- 2.4.1.1 Sparse Autoencoder -- 2.4.1.2 Denoising Autoencoder -- 2.4.1.3 Contractive Autoencoder -- 2.4.1.4 Convolutional Autoencoder -- 2.4.1.5 Variational Autoencoder -- 2.4.2 Generative Adversarial Networks -- 2.4.3 Boltzmann Machines -- 2.4.3.1 Restricted Boltzmann Machine -- 2.4.3.2 Deep Belief Network -- 2.4.3.3 Deep Boltzmann Machine -- 2.5 Graph-Based Models -- 2.5.1 Graph Neural Network -- 2.5.2 Graph Convolutional Network -- 2.6 Conclusion -- References -- Chapter 3: Deep Discriminative Session-Based Recommender System -- 3.1 Introduction -- 3.2 Fundamentals -- 3.2.1 Datasets. 327 $a3.2.2 Evaluation -- 3.3 Session-Based Recommender System Using RNN -- 3.3.1 Why RNN? -- 3.3.2 GRU Approaches -- 3.3.3 LSTM Approaches -- 3.4 Session-Based Recommender System Using CNN -- 3.4.1 Why CNN? -- 3.4.2 CNN Approaches -- 3.5 Discussion -- 3.6 Conclusion -- References -- Chapter 4: Deep Generative Session-Based Recommender System -- 4.1 Introduction -- 4.2 Fundamentals -- 4.2.1 Datasets -- 4.2.2 Evaluation -- 4.3 Session-Based Recommender System Using Autoencoder -- 4.3.1 Why Autoencoder? -- 4.3.2 Autoencoder Approaches -- 4.4 Session-Based Recommender System Using GAN -- 4.4.1 Why GAN? -- 4.4.2 GAN Approaches -- 4.5 Session-Based Recommender System Using FBM -- 4.5.1 Why Flow-Based Models? -- 4.5.2 Flow-Based Approaches -- 4.6 Discussion -- 4.7 Conclusion -- References -- Chapter 5: Hybrid/Advanced Session-Based Recommender Systems -- 5.1 Introduction -- 5.2 Fundamentals -- 5.2.1 Datasets -- 5.2.2 Evaluation -- 5.3 SBRS Using Hybrid Deep Neural Networks -- 5.3.1 Why Hybrid Deep Neural Network? -- 5.3.2 Approaches Based on CNN and LSTM -- 5.3.3 Approaches Based on CNN and GRU -- 5.3.4 Approaches Based on RNN and Autoencoder -- 5.4 SBRS Using Deep Graph Neural Network -- 5.4.1 Why Graph Neural Network? -- 5.4.2 Approaches Based on GNN -- 5.4.3 Approaches Based on GNN and RNN -- 5.4.4 Approaches Based on GCN -- 5.5 SBRS Using Deep Reinforcement Learning -- 5.5.1 Why Deep Reinforcement Learning? -- 5.5.2 Approaches Based on Deep Q-Learning -- 5.5.3 Approaches Based on DRL and RNN -- 5.5.4 Approaches Based on DRL and CNN -- 5.5.5 Approaches Based on DRL and GAN -- 5.6 Discussion -- 5.7 Conclusion -- References -- Chapter 6: Learning to Rank in Session-Based Recommender Systems -- 6.1 Introduction -- 6.2 Fundamentals -- 6.2.1 Ranking Creation -- 6.2.2 Ranking Aggregation -- 6.2.3 Datasets -- 6.3 Ranking Creation -- 6.3.1 Pointwise Methods. 327 $a6.3.1.1 Pointwise Methods in Information Retrieval -- 6.3.1.2 Pointwise Methods in Recommender Systems -- 6.3.2 Pairwise Methods -- 6.3.2.1 Pairwise Methods in Information Retrieval -- 6.3.2.2 Pairwise Methods in Recommender Systems -- 6.3.3 Listwise Methods -- 6.3.3.1 Listwise Methods in Information Retrieval -- 6.3.3.2 Listwise Methods in Recommender Systems -- 6.3.4 Hybrid Methods -- 6.4 Ranking Aggregation -- 6.4.1 Ranking Aggregation Methods in Information Retrieval -- 6.4.2 Ranking Aggregation Methods in Recommender Systems -- 6.5 Discussion -- 6.6 Conclusion -- References -- Summary -- Index. 606 $aDeep learning (Machine learning) 606 $aRecommender systems (Information filtering) 615 0$aDeep learning (Machine learning) 615 0$aRecommender systems (Information filtering) 676 $a006.31 700 $aRavanmehr$b Reza$01586419 702 $aMohamadrezaei$b Rezvan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799492003321 996 $aSession-Based Recommender Systems Using Deep Learning$93872811 997 $aUNINA