Personalized Machine Learning
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Personalized Machine Learning
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Author : Julian McAuley
language : en
Publisher: Cambridge University Press
Release Date : 2022-02-03
Personalized Machine Learning written by Julian McAuley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Computers categories.
Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.
Deep Learning For Personalized Healthcare Services
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Author : Vishal Jain
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2021-10-25
Deep Learning For Personalized Healthcare Services written by Vishal Jain and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-25 with Computers categories.
This book uncovers the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms, enabling the healthcare industry to develop meaningful and cost-effective services. This requires effective understanding, application and amalgamation of deep learning with several other computing technologies, such as machine learning, data mining, and natural language processing.
Personalization Techniques And Recommender Systems
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Author : Matthew Y. Ma
language : en
Publisher: World Scientific
Release Date : 2008
Personalization Techniques And Recommender Systems written by Matthew Y. Ma and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed. The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems. This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems. Sample Chapter(s). Personalization-Privacy Tradeoffs in Adaptive Information Access (865 KB). Contents: User Modeling and Profiling: Personalization-Privacy Tradeoffs in Adaptive Information Access (B Smyth); A Deep Evaluation of Two Cognitive User Models for Personalized Search (F Gasparetti & A Micarelli); Unobtrusive User Modeling for Adaptive Hypermedia (H J Holz et al.); User Modelling Sharing for Adaptive e-Learning and Intelligent Help (K Kabassi et al.); Collaborative Filtering: Experimental Analysis of Multiattribute Utility Collaborative Filtering on a Synthetic Data Set (N Manouselis & C Costopoulou); Efficient Collaborative Filtering in Content-Addressable Spaces (S Berkovsky et al.); Identifying and Analyzing User Model Information from Collaborative Filtering Datasets (J Griffith et al.); Content-Based Systems, Hybrid Systems and Machine Learning Methods: Personalization Strategies and Semantic Reasoning: Working in Tandem in Advanced Recommender Systems (Y Blanco-Fernindez et al.); Content Classification and Recommendation Techniques for Viewing Electronic Programming Guide on a Portable Device (J Zhu et al.); User Acceptance of Knowledge-Based Recommenders (A Felfernig et al.); Using Restricted Random Walks for Library Recommendations and Knowledge Space Exploration (M Franke & A Geyer-Schulz); An Experimental Study of Feature Selection Methods for Text Classification (G Uchyigit & K Clark). Readership: Researchers and graduate students in machine learning and databases/information science.
One To One Personalization In The Age Of Machine Learning
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Author : Karl Wirth
language : en
Publisher: Bookbaby
Release Date : 2017-12-16
One To One Personalization In The Age Of Machine Learning written by Karl Wirth and has been published by Bookbaby this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-16 with Business & Economics categories.
In a world cluttered with messages competing for people's attention all of the time, marketers must surface relevant information if they want to capture the attention of their consumers or business buyers. And as consumers experience personalized experiences from other companies like Amazon, Netflix and Spotify, they grow to expect it from all the other companies they interact with, regardless of industry. One-to-one personalization is about tailoring an experience to a visitor or customer at the individual level. The experience could be on a website, mobile app, email, in-person, or any other channel where a person interacts with your brand or company. In contrast to a one-to-all experience (one that is the same for everyone) or a one-to-many experience (one that is targeted to a segment or group of people), a one-to-one experience is truly unique for each person. While marketers have dreamed of delivering one-to-one experiences for over 25 years, it has not been possible without machine learning. Machine learning can combine many different sources of data, draw insights about what that data says about an individual, and determine the most relevant experience to deliver -- in a far more scalable way than has ever been possible in the past In One-to-One Personalization in the Age of Machine Learning, discover what one-to-one personalization is all about, how it has evolved and what the future entails. Learn how it's driven by machine learning, delivered across channels and powered by in-depth customer data. Get inspired by the potential for your business and gain insights on how to develop your own personalization strategy and program. Discover how to turn the one-to-one dream into a reality.
Intelligent Techniques For Web Personalization
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Author : Bamshad Mobasher
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-04
Intelligent Techniques For Web Personalization written by Bamshad Mobasher and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-04 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the Second Workshop on Intelligent Techniques in Web Personalization, ITWP 2003, held in Acapulco, Mexico in August 2003 as part of IJCAI 2003, the 18th International Joint Conference on Artificial Intelligence. The 17 revised full papers presented were carefully selected and include extended versions of some of the papers presented at the ITWP 2003 workshop as well as a number of invited chapters by leading researchers in the field of Intelligent Techniques for Web Personalization. The papers are organized in topical sections on user modelling, recommender systems, enabling technologies, personalized information access, and systems and applications.
Teaching Machines
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Author : Audrey Watters
language : en
Publisher: MIT Press
Release Date : 2023-02-07
Teaching Machines written by Audrey Watters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-07 with Education categories.
How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.
Federated Learning
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Author : Qiang Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Federated Learning written by Qiang Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Artificial Intelligence And Educational Personalization
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Author : Santos Costa
language : en
Publisher: Santos Costa
Release Date : 2023-10-14
Artificial Intelligence And Educational Personalization written by Santos Costa and has been published by Santos Costa this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-14 with Computers categories.
Artificial intelligence is revolutionizing the way we learn and teach. In this book, dive into the fascinating world of personalized learning with artificial intelligence and discover how this synergy is transforming education on a global scale. Throughout this book, we explore the fundamental concepts of personalized learning, presenting innovative approaches that empower educators to meet the unique needs of each student. Understand the crucial role of artificial intelligence in customizing educational content, adapting to individual preferences and pace of learning. We will enter the world of AI applied to education, knowing the various specific applications of this technology in educational institutions around the world. Successful case studies will reveal how schools and universities have achieved significant improvements in academic performance, student engagement and school retention. However, we will not ignore the challenges and ethical considerations that come with adopting AI in education. We will explore the barriers to implementing personalized learning with artificial intelligence and discuss how to mitigate algorithmic bias and ensure the privacy of student data. In addition, we will envision the exciting future of personalized learning with AI. Learn about the technological advancements driving this innovative approach and the trends that will shape the educational landscape in the coming years. This book is a must-read for educators, educational managers, researchers, and anyone interested in the future of education. By exploring the transformative possibilities of artificial intelligence in personalized learning, you'll be inspired to drive positive change in the way we learn and teach. Ready to embark on a journey towards personalized, inclusive and innovation-driven education? Come with us and discover how artificial intelligence is shaping the future of education and empowering every student to reach their full potential.
Building A Personalized Model Exploring Topic Of Interest Through A Machine Learning Pipeline
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Author :
language : en
Publisher:
Release Date : 2023
Building A Personalized Model Exploring Topic Of Interest Through A Machine Learning Pipeline written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Personalized Human Computer Interaction
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Author : Mirjam Augstein
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-08-07
Personalized Human Computer Interaction written by Mirjam Augstein and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-07 with Computers categories.
Personalized and adaptive systems employ user models to adapt content, services, interaction or navigation to individual users’ needs. User models can be inferred from implicitly observed information, such as the user’s interaction history or current location, or from explicitly entered information, such as user profile data or ratings. Applications of personalization include item recommendation, location-based services, learning assistance and the tailored selection of interaction modalities. With the transition from desktop computers to mobile devices and ubiquitous environments, the need for adapting to changing contexts is even more important. However, this also poses new challenges concerning privacy issues, user control, transparency, and explainability. In addition, user experience and other human factors are becoming increasingly important. This book describes foundations of user modeling, discusses user interaction as a basis for adaptivity, and showcases several personalization approaches in a variety of domains, including music recommendation, tourism, and accessible user interfaces.