Recommender Systems Handbook

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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 : 47,5 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 Handbook

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

Author : Francesco Ricci,Lior Rokach,Bracha Shapira
Publisher : Unknown
Page : 128 pages
File Size : 51,6 Mb
Release : 2015
Category : Electronic
ISBN : 1489976388

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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 : 47,8 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.

Recommendation Systems in Software Engineering

Martin P. Robillard,Walid Maalej,Robert J. Walker,Thomas Zimmermann
Recommendation Systems in Software Engineering Book PDF

Author : Martin P. Robillard,Walid Maalej,Robert J. Walker,Thomas Zimmermann
Publisher : Springer Science & Business
Page : 562 pages
File Size : 50,6 Mb
Release : 2014-04-30
Category : Computers
ISBN : 9783642451355

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Recommendation Systems in Software Engineering by Martin P. Robillard,Walid Maalej,Robert J. Walker,Thomas Zimmermann Pdf

With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.

Fashion Recommender Systems

Nima Dokoohaki
Fashion Recommender Systems Book PDF

Author : Nima Dokoohaki
Publisher : Springer Nature
Page : 144 pages
File Size : 43,9 Mb
Release : 2020-11-04
Category : Science
ISBN : 9783030552183

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Fashion Recommender Systems by Nima Dokoohaki Pdf

This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

Practical Recommender Systems

Kim Falk
Practical Recommender Systems Book PDF

Author : Kim Falk
Publisher : Simon and Schuster
Page : 743 pages
File Size : 55,8 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

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 : 49,5 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.

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 : 40,5 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.

Educational Recommender Systems and Technologies: Practices and Challenges

Santos, Olga C.
Educational Recommender Systems and Technologies Practices and Challenges Book PDF

Author : Santos, Olga C.
Publisher : IGI Global
Page : 362 pages
File Size : 47,7 Mb
Release : 2011-12-31
Category : Education
ISBN : 9781613504901

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Educational Recommender Systems and Technologies: Practices and Challenges by Santos, Olga C. Pdf

Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.

Recommender Systems in Fashion and Retail

Nima Dokoohaki,Shatha Jaradat,Humberto Jesús Corona Pampín,Reza Shirvany
Recommender Systems in Fashion and Retail Book PDF

Author : Nima Dokoohaki,Shatha Jaradat,Humberto Jesús Corona Pampín,Reza Shirvany
Publisher : Springer Nature
Page : 160 pages
File Size : 44,8 Mb
Release : 2021-03-23
Category : Computers
ISBN : 9783030661038

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Recommender Systems in Fashion and Retail by Nima Dokoohaki,Shatha Jaradat,Humberto Jesús Corona Pampín,Reza Shirvany Pdf

This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Recommender Systems

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

Author : Dietmar Jannach,Associate Professor Markus Zanker,Professor Alexander Felfernig,Gerhard Friedrich
Publisher : Unknown
Page : 353 pages
File Size : 42,6 Mb
Release : 2014-05-14
Category : Personal communication service systems
ISBN : 0511918844

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

This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

Statistical Methods for Recommender Systems

Deepak K. Agarwal,Bee-Chung Chen
Statistical Methods for Recommender Systems Book PDF

Author : Deepak K. Agarwal,Bee-Chung Chen
Publisher : Cambridge University Press
Page : 307 pages
File Size : 40,9 Mb
Release : 2016-02-24
Category : Computers
ISBN : 9781316565131

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Statistical Methods for Recommender Systems by Deepak K. Agarwal,Bee-Chung Chen Pdf

Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Recommender Systems for Learning

Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Erik Duval
Recommender Systems for Learning Book PDF

Author : Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Erik Duval
Publisher : Springer Science & Business Media
Page : 76 pages
File Size : 46,7 Mb
Release : 2012-08-28
Category : Computers
ISBN : 9781461443612

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Recommender Systems for Learning by Nikos Manouselis,Hendrik Drachsler,Katrien Verbert,Erik Duval Pdf

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Dehuri, Satchidananda
Intelligent Techniques in Recommendation Systems Contextual Advancements and New Methods Book PDF

Author : Dehuri, Satchidananda
Publisher : IGI Global
Page : 351 pages
File Size : 48,9 Mb
Release : 2012-11-30
Category : Computers
ISBN : 9781466625433

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Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods by Dehuri, Satchidananda Pdf

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.

Collaborative Filtering Recommender Systems

Michael D. Ekstrand,John T. Riedl,Joseph A. Konstan
Collaborative Filtering Recommender Systems Book PDF

Author : Michael D. Ekstrand,John T. Riedl,Joseph A. Konstan
Publisher : Now Publishers Inc
Page : 104 pages
File Size : 48,9 Mb
Release : 2011
Category : Computers
ISBN : 9781601984425

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Collaborative Filtering Recommender Systems by Michael D. Ekstrand,John T. Riedl,Joseph A. Konstan Pdf

Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.