Titlebook: Session-Based Recommender Systems Using Deep Learning; Reza Ravanmehr,Rezvan Mohamadrezaei Book 2024 The Editor(s) (if applicable) and The

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Introduction to Session-Based Recommender Systems,techniques of traditional recommender systems, we focus on the fundamental concepts, descriptions, challenges, and approaches of SBRS and clarify the differences between SBRS and SRS (sequential recommender system) from various aspects.

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Introduction to Session-Based Recommender Systems,’ profiles and their long-term interests are crucial challenges of these systems. A session-based recommender system (SBRS) was developed to solve these problems and received much attention from the research community. In this chapter of the book, after presenting an overview of the definitions and

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Deep Learning Overview,ignificantly employed in effectively extracting hidden patterns from vast amounts of data and modeling interdependent variables to solve complex problems. Since this book aims to discuss the session-based recommender system approaches using deep learning models, brief explanations of various deep ne

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Deep Discriminative Session-Based Recommender System,works (RNNs), including GRU and LSTM. On the other hand, convolutional neural networks (CNNs) provide very effective solutions for modeling sequential data when sequence elements are associated with complex features. As a result, we discuss different deep discriminative models in SBRS in this chapte

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Deep Generative Session-Based Recommender System,ive solutions for a session-based recommender system (SBRS). In addition, in real-world scenarios, users usually only select a limited number of items, and their interactions in response to items are very sparse. Deep generative models that produce more training samples can help reduce the data spar

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Hybrid/Advanced Session-Based Recommender Systems,ty of deep neural networks, many neural network blocks can be integrated to construct more robust and accurate models. Many session-based recommender system utilize hybrid deep neural network models. There are also several advanced deep learning approaches that are very popular in SBRS, including gr

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Learning to Rank in Session-Based Recommender Systems,results in information retrieval and recommender systems automatically called learning to rank (LtR). Two main important subsets of LtR systems include ranking creation and ranking aggregation. This chapter of the book discussed different models of LtR in information retrieval, recommender systems,

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