Recommender Systems

Author: Dietmar Jannach
Publisher: Cambridge University Press
ISBN: 1139492594
Format: PDF, Mobi
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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.

Group Recommender Systems

Author: Alexander Felfernig
Publisher: Springer
ISBN: 3319750674
Format: PDF, Docs
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This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of state-of-the-art algorithms, an overview of industrial applications, an inclusion of the aspects of decision biases in groups, and corresponding de-biasing approaches. The book includes a discussion of basic group recommendation methods, aspects of human decision making in groups, and related applications. A discussion of open research issues is included to inspire new related research. The book serves as a reference for researchers and practitioners working on group recommendation related topics.

Recommender Systems Handbook

Author: Francesco Ricci
Publisher: Springer
ISBN: 148997637X
Format: PDF, Kindle
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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

Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319296590
Format: PDF, Docs
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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.

Statistical Methods for Recommender Systems

Author: Deepak K. Agarwal
Publisher: Cambridge University Press
ISBN: 1316565130
Format: PDF, ePub
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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.

Practical Recommender Systems

Author: Kim Falk
Publisher:
ISBN: 9781617292705
Format: PDF, ePub
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Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Building a Recommendation System with R

Author: Suresh K. Gorakala
Publisher: Packt Publishing Ltd
ISBN: 1783554509
Format: PDF, Kindle
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Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

Building Recommendation Engines

Author: Suresh Kumar Gorakala
Publisher: Packt Publishing Ltd
ISBN: 1785883534
Format: PDF, ePub, Mobi
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Understand your data and user preferences to make intelligent, accurate, and profitable decisions About This Book A step-by-step guide to building recommendation engines that are personalized, scalable, and real time Get to grips with the best tool available on the market to create recommender systems This hands-on guide shows you how to implement different tools for recommendation engines, and when to use which Who This Book Is For This book caters to beginners and experienced data scientists looking to understand and build complex predictive decision-making systems, recommendation engines using R, Python, Spark, Neo4j, and Hadoop. What You Will Learn Build your first recommendation engine Discover the tools needed to build recommendation engines Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations Create efficient decision-making systems that will ease your work Familiarize yourself with machine learning algorithms in different frameworks Master different versions of recommendation engines from practical code examples Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others In Detail A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best. During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book! Style and approach This book follows a step-by-step practical approach where users will learn to build recommendation engines with increasing complexity in every chapter

Collaborative Filtering Recommender Systems

Author: Michael D. Ekstrand
Publisher: Now Publishers Inc
ISBN: 1601984421
Format: PDF, Kindle
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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.