Personalization Techniques And Recommender Systems
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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.
Recommender Systems
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2016-03-28
Recommender Systems written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-28 with Computers categories.
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
Applications Of Data Mining To Electronic Commerce
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Author : Ronny Kohavi
language : en
Publisher: Springer
Release Date : 2001-02-28
Applications Of Data Mining To Electronic Commerce written by Ronny Kohavi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-28 with Business & Economics categories.
Applications of Data Mining to Electronic Commerce brings together in one place important contributions and up-to-date research results in this fast moving area. Applications of Data Mining to Electronic Commerce serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
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.
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
Practical Recommender Systems
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Author : Kim Falk
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-18
Practical Recommender Systems written by Kim Falk and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-18 with Computers categories.
Summary Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. About the Book Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. What's inside How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the Reader Readers need intermediate programming and database skills. About the Author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Table of Contents PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS What is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate them Non-personalized recommendations The user (and content) who came in from the cold PART 2 - RECOMMENDER ALGORITHMS Finding similarities among users and among content Collaborative filtering in the neighborhood Evaluating and testing your recommender Content-based filtering Finding hidden genres with matrix factorization Taking the best of all algorithms: implementing hybrid recommenders Ranking and learning to rank Future of recommender systems
Intelligent Techniques In Recommendation Systems Contextual Advancements And New Methods
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Author : Dehuri, Satchidananda
language : en
Publisher: IGI Global
Release Date : 2012-11-30
Intelligent Techniques In Recommendation Systems Contextual Advancements And New Methods written by Dehuri, Satchidananda and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-30 with Computers categories.
Although recommendation systems have become a vital research area in the fields of cognitive science, approximation theory, information retrieval and management sciences, they still require improvements to make recommendation methods more effective and intelligent. Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how this could improve this field of study.
Educational Recommender Systems And Technologies
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Author : Olga C. Santos
language : en
Publisher:
Release Date : 2012
Educational Recommender Systems And Technologies written by Olga C. Santos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Educational technology categories.
"This book aims to provide a comprehensive review of state-of-the-art practices for educational recommender systems, as well as the challenges to achieve their actual deployment"--Provided by publisher.
Special Issue
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Author : M. Y.C. Ma
language : en
Publisher:
Release Date : 2007
Special Issue written by M. Y.C. Ma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
E Business Management
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Author : Michael J. Shaw
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11
E Business Management written by Michael J. Shaw 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 2006-04-11 with Computers categories.
E-Business Management: Integration of Web Technologies with Business Models contains a collection of articles by leading information systems researchers on important topics related to the development of e-business. The goal is to enhance the understanding of the state of the art in e-business, including the most current and forward-looking research. The book emphasizes both business practices and academic research made possible by the recent rapid advances in the applications of e-business technology. The book should help graduate students, researchers, and practitioners understand major e-business developments, how they will transform businesses, and the strategic implications to be drawn.
Recommender Systems Handbook
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Author : Francesco Ricci
language : en
Publisher: Springer
Release Date : 2015-11-17
Recommender Systems Handbook written by Francesco Ricci and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-17 with Computers categories.
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.