Session Based Recommender Systems Using Deep Learning
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Session Based Recommender Systems Using Deep Learning
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Author : Reza Ravanmehr
language : en
Publisher: Springer Nature
Release Date : 2024-01-21
Session Based Recommender Systems Using Deep Learning written by Reza Ravanmehr and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-21 with Technology & Engineering categories.
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.
Recommender Systems Handbook
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Author : Francesco Ricci
language : en
Publisher: Springer Nature
Release Date : 2022-04-21
Recommender Systems Handbook written by Francesco Ricci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-21 with Computers categories.
This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.
Building Recommender Systems With Machine Learning And Ai Help People Discover New Products And Content With Deep Learning Neural Networks And Mach
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Author : Frank Kane
language : en
Publisher:
Release Date : 2018-08-11
Building Recommender Systems With Machine Learning And Ai Help People Discover New Products And Content With Deep Learning Neural Networks And Mach written by Frank Kane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-11 with Computers categories.
Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies.You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you'll have access to all of the source code associated with it as well.We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover: -Building a recommendation engine-Evaluating recommender systems-Content-based filtering using item attributes-Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF-Model-based methods including matrix factorization and SVD-Applying deep learning, AI, and artificial neural networks to recommendations-Session-based recommendations with recursive neural networks-Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines-Real-world challenges and solutions with recommender systems-Case studies from YouTube and Netflix-Building hybrid, ensemble recommendersThis comprehensive book takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.The coding exercises for this book use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this book successfully. We also include a short introduction to deep learning, Tensorfow, and Keras if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms.Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is!
Proceedings Of The 1st Workshop On Deep Learning For Recommender Systems
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Author : Alexandros Karatzoglou
language : en
Publisher:
Release Date : 2016-09-15
Proceedings Of The 1st Workshop On Deep Learning For Recommender Systems written by Alexandros Karatzoglou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-15 with Computer science categories.
Workshop on Deep Learning for Recommender Systems Sep 15, 2016-Sep 15, 2016 Boston, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Explainable Recommendation
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Author : Yongfeng Zhang
language : en
Publisher:
Release Date : 2020-03-10
Explainable Recommendation written by Yongfeng Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-10 with Computers categories.
In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.
Recommender System With Machine Learning And Artificial Intelligence
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Author : Sachi Nandan Mohanty
language : en
Publisher: John Wiley & Sons
Release Date : 2020-07-08
Recommender System With Machine Learning And Artificial Intelligence written by Sachi Nandan Mohanty and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
Recommender Systems
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Author : Dongsheng Li
language : en
Publisher: Springer Nature
Release Date :
Recommender Systems written by Dongsheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Building Recommender Systems With Machine Learning And Ai
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Author : Frank Kane
language : en
Publisher:
Release Date : 2018
Building Recommender Systems With Machine Learning And Ai written by Frank Kane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.
Artificial Intelligence And Speech Technology
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Author : Amita Dev
language : en
Publisher: Springer Nature
Release Date : 2022-01-28
Artificial Intelligence And Speech Technology written by Amita Dev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-28 with Computers categories.
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications
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Author : Tran Khanh Dang
language : en
Publisher: Springer Nature
Release Date : 2022-11-19
Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications written by Tran Khanh Dang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-19 with Computers categories.
This book constitutes the refereed proceedings of the 9th International Conference on Future Data and Security Engineering, FDSE 2022, held in Ho Chi Minh City, Vietnam, during November 23–25, 2022. The 41 full papers(including 4 invited keynotes) and 12 short papers included in this book were carefully reviewed and selected from 170 submissions. They were organized in topical sections as follows: invited keynotes; big data analytics and distributed systems; security and privacy engineering; machine learning and artificial intelligence for security and privacy; smart city and industry 4.0 applications; data analytics and healthcare systems; and security and data engineering.