Recommender Systems Advanced Developments

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Recommender Systems: Advanced Developments

Jie Lu,Qian Zhang,Guang-quan Zhang
Recommender Systems Advanced Developments Book PDF

Author : Jie Lu,Qian Zhang,Guang-quan Zhang
Publisher : World Scientific
Page : 362 pages
File Size : 45,5 Mb
Release : 2020-08-04
Category : Computers
ISBN : 9789811224645

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Recommender Systems: Advanced Developments by Jie Lu,Qian Zhang,Guang-quan Zhang Pdf

Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Recommender Systems Handbook

Francesco Ricci,Lior Rokach,Bracha Shapira
Recommender Systems Handbook Book PDF

Author : Francesco Ricci,Lior Rokach,Bracha Shapira
Publisher : Springer Nature
Page : 1053 pages
File Size : 40,9 Mb
Release : 2022-04-21
Category : Computers
ISBN : 9781071621974

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Recommender Systems Handbook by Francesco Ricci,Lior Rokach,Bracha Shapira Pdf

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.

Recommender System with Machine Learning and Artificial Intelligence

Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta
Recommender System with Machine Learning and Artificial Intelligence Book PDF

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 40,9 Mb
Release : 2020-07-08
Category : Computers
ISBN : 9781119711575

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Recommender System with Machine Learning and Artificial Intelligence by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta Pdf

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 Handbook

Francesco Ricci,Lior Rokach,Bracha Shapira
Recommender Systems Handbook Book PDF

Author : Francesco Ricci,Lior Rokach,Bracha Shapira
Publisher : Springer
Page : 1003 pages
File Size : 55,5 Mb
Release : 2015-11-17
Category : Computers
ISBN : 9781489976376

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Recommender Systems Handbook by Francesco Ricci,Lior Rokach,Bracha Shapira Pdf

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.

Recommender Systems

Charu C. Aggarwal
Recommender Systems Book PDF

Author : Charu C. Aggarwal
Publisher : Springer
Page : 498 pages
File Size : 40,6 Mb
Release : 2016-03-28
Category : Computers
ISBN : 9783319296593

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Recommender Systems by Charu C. Aggarwal Pdf

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.

Recommender Systems

Gérald Kembellec,Ghislaine Chartron,Imad Saleh
Recommender Systems Book PDF

Author : Gérald Kembellec,Ghislaine Chartron,Imad Saleh
Publisher : John Wiley & Sons
Page : 256 pages
File Size : 45,5 Mb
Release : 2014-12-04
Category : Computers
ISBN : 9781119054245

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Recommender Systems by Gérald Kembellec,Ghislaine Chartron,Imad Saleh Pdf

Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

Personalization Techniques and Recommender Systems

Gulden Uchyigit,Matthew Y. Ma
Personalization Techniques and Recommender Systems Book PDF

Author : Gulden Uchyigit,Matthew Y. Ma
Publisher : World Scientific
Page : 334 pages
File Size : 43,8 Mb
Release : 2008
Category : Science
ISBN : 9789812797018

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Personalization Techniques and Recommender Systems by Gulden Uchyigit,Matthew Y. Ma Pdf

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.

Social Network-Based Recommender Systems

Daniel Schall
Social Network Based Recommender Systems Book PDF

Author : Daniel Schall
Publisher : Springer
Page : 126 pages
File Size : 43,6 Mb
Release : 2015-09-23
Category : Computers
ISBN : 9783319227351

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Social Network-Based Recommender Systems by Daniel Schall Pdf

This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on ‘social brokers’ are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.

Group Recommender Systems

Alexander Felfernig,Ludovico Boratto,Martin Stettinger,Marko Tkalčič
Group Recommender Systems Book PDF

Author : Alexander Felfernig,Ludovico Boratto,Martin Stettinger,Marko Tkalčič
Publisher : Springer Nature
Page : 180 pages
File Size : 48,6 Mb
Release : 2023-11-27
Category : Technology & Engineering
ISBN : 9783031449437

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Group Recommender Systems by Alexander Felfernig,Ludovico Boratto,Martin Stettinger,Marko Tkalčič Pdf

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.

Recommender Systems

Dietmar Jannach,Markus Zanker,Alexander Felfernig,Gerhard Friedrich
Recommender Systems Book PDF

Author : Dietmar Jannach,Markus Zanker,Alexander Felfernig,Gerhard Friedrich
Publisher : Cambridge University Press
Page : 352 pages
File Size : 55,6 Mb
Release : 2010-09-30
Category : Computers
ISBN : 0521493366

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Recommender Systems by Dietmar Jannach,Markus Zanker,Alexander Felfernig,Gerhard Friedrich Pdf

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Building Effective Recommender Systems

Bracha Shapira,Lior Rokach
Building Effective Recommender Systems Book PDF

Author : Bracha Shapira,Lior Rokach
Publisher : Springer
Page : 128 pages
File Size : 45,6 Mb
Release : 2010-11-16
Category : Computers
ISBN : 1441901116

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Building Effective Recommender Systems by Bracha Shapira,Lior Rokach Pdf

Supporting the user with the decision-making and buying process, recommender systems have proven to be a valuable means for online users to cope with the virtual information overload. It is one of the most powerful and popular tools in electronic commerce available today. Development of recommender systems is a multi-disciplinary effort, involving experts from various fields such as data mining, artificial intelligence, statistics, human computer interaction, information retrieval/technology, and adaptive user interfaces. This book covers all aspects and important techniques for recommender systems, such as collaborative filtering, content based techniques, popular hybrid approaches and a detailed tutorial of recommender systems software. Designed for industry researchers in the fields of information technology, e-commerce, information retrieval, data mining, databases and statistics, and practitioners, this book is also suitable for advanced-level students in computer science as a secondary textbook.

Session-Based Recommender Systems Using Deep Learning

Reza Ravanmehr,Rezvan Mohamadrezaei
Session Based Recommender Systems Using Deep Learning Book PDF

Author : Reza Ravanmehr,Rezvan Mohamadrezaei
Publisher : Springer Nature
Page : 314 pages
File Size : 47,7 Mb
Release : 2024-01-21
Category : Technology & Engineering
ISBN : 9783031425592

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Session-Based Recommender Systems Using Deep Learning by Reza Ravanmehr,Rezvan Mohamadrezaei Pdf

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.

Encyclopedia of Machine Learning

Claude Sammut,Geoffrey I. Webb
Encyclopedia of Machine Learning Book PDF

Author : Claude Sammut,Geoffrey I. Webb
Publisher : Springer Science & Business Media
Page : 1061 pages
File Size : 52,9 Mb
Release : 2011-03-28
Category : Computers
ISBN : 9780387307688

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Encyclopedia of Machine Learning by Claude Sammut,Geoffrey I. Webb Pdf

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Hands-On Recommendation Systems with Python

Rounak Banik
Hands On Recommendation Systems with Python Book PDF

Author : Rounak Banik
Publisher : Packt Publishing Ltd
Page : 141 pages
File Size : 49,7 Mb
Release : 2018-07-31
Category : Computers
ISBN : 9781788992534

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Hands-On Recommendation Systems with Python by Rounak Banik Pdf

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Practical Recommender Systems

Kim Falk
Practical Recommender Systems Book PDF

Author : Kim Falk
Publisher : Simon and Schuster
Page : 743 pages
File Size : 51,7 Mb
Release : 2019-01-18
Category : Computers
ISBN : 9781638353980

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Practical Recommender Systems by Kim Falk Pdf

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