recommender systems learning (40 Ergebnisse)

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Zustand: Good. Used book that is in clean, average condition without any missing pages.

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235 mm x 155 mm, 0 g. XV, 125 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Intelligent Systems Reference Library ; 92. Sprache: Englisch.

ed. 2014. XIV, 306 p. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stemped. Sprache: Englisch.

Paperback. Zustand: Very Good. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

paperback. Zustand: Fine.

Paperback. Zustand: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.

Zustand: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 223 pages. 9.25x6.25x0.55 inches. In Stock.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: New. 2021. 2nd ed. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware -Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.Books on Demand GmbH, Überseering 33, 22297 Hamburg 152 pp. Englisch.

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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Are you confused about what all the rage behind artificial intelligence is and would like to learn more?This book covers everything from machine learning to robotics and the internet of things.You can use it as a nifty guidebook whenever you come across news headlines that talk about some new advancement in AI by Google or Facebook.By the time you finish reading, you will be aware of what artificial neural networks are, how gradient descent and back propagation work, and what deep learning is.You will also learn a comprehensive history of AI, from the first invention of automations in antiquity to the driver-less cars of today.Here's just a tiny fraction of what you'll discover: Understand how machines can "think" and how they learn Learn the five reasons why experts are warning us about AI research Find the answers to the top six myths of artificial intelligence Learn what neural networks are and how they work, the "brains" of machine learning Understand reinforcement learning and how it is used to teach machine learning systems through experience Become up-to-date with the current state-of-the-art artificial intelligence methods that use deep learning Learn the basics of recommender systems Expand your current view of machines and what is possible with modern robotics Enter the vast world of the internet of things technologies Find out why AI is the new business degree And much, much more!If you want to learn more about artificial intelligence, then scroll up and click "add to cart"!

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests. The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems. The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items. Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 1.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. Yoüll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. Yoüll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. Yoüll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark¿s latest ML library. After completing this book, you will understand how to use PySpark¿s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark¿s machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Recommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

Taschenbuch. Zustand: Neu. Learning User-Adapted Strategies in Conversational Recommender Systems | Application of Reinforcement Learning To E-commerce Portals For Learning a System Behavior That Is Adapted To The Users In An Interaction Context | Tariq Mahmood | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639079791 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Paperback. Zustand: Brand New. 180 pages. 8.66x5.91x0.41 inches. In Stock.

Paperback. Zustand: Brand New. reprint edition. 320 pages. 9.25x6.10x0.71 inches. In Stock.

Hardcover. Zustand: Neu. Neu Neuware, auf Lager - As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

Hardcover. Zustand: Brand New. 320 pages. 9.50x6.25x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Machine Learning Paradigms | Applications in Recommender Systems | Aristomenis S. Lampropoulos (u. a.) | Taschenbuch | Previously published in hardcover | xv | Englisch | 2016 | Springer | EAN 9783319384962 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Buch. Zustand: Neu. Neuware -As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Paperback. Zustand: Brand New. reprint edition. 140 pages. 9.25x6.10x0.34 inches. In Stock.