Web Recommendations Systems
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| Author | : K. R. Venugopal |
| Publisher | : Springer Nature |
| Total Pages | : 178 |
| Release | : 2020-03-02 |
| Genre | : Computers |
| ISBN | : 9811525137 |
This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse applications, including query log mining, social networking, news recommendations and computational advertising, and with the explosive growth of Web content, Web recommendations have become a critical aspect of all search engines. The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with valuable insights into Web recommender systems.
| Author | : Michael Schrage |
| Publisher | : MIT Press |
| Total Pages | : 306 |
| Release | : 2020-09-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0262358786 |
How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."
| Author | : Peter Brusilovski |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 770 |
| Release | : 2007-04-24 |
| Genre | : Computers |
| ISBN | : 3540720782 |
This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field.
| Author | : Charu C. Aggarwal |
| Publisher | : Springer |
| Total Pages | : 518 |
| Release | : 2016-03-28 |
| Genre | : Computers |
| ISBN | : 3319296590 |
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.
| Author | : Rounak Banik |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 141 |
| Release | : 2018-07-31 |
| Genre | : Computers |
| ISBN | : 1788992539 |
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.
| Author | : Francesco Ricci |
| Publisher | : Springer |
| Total Pages | : 1008 |
| Release | : 2015-11-17 |
| Genre | : Computers |
| ISBN | : 148997637X |
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.
| Author | : Jure Leskovec |
| Publisher | : Cambridge University Press |
| Total Pages | : 480 |
| Release | : 2014-11-13 |
| Genre | : Computers |
| ISBN | : 1107077230 |
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
| Author | : Michael D. Ekstrand |
| Publisher | : Now Publishers Inc |
| Total Pages | : 104 |
| Release | : 2011 |
| Genre | : Computers |
| ISBN | : 1601984421 |
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.
| Author | : Niall Richard Murphy |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 552 |
| Release | : 2016-03-23 |
| Genre | : |
| ISBN | : 1491951176 |
The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use
| Author | : Sachi Nandan Mohanty |
| Publisher | : John Wiley & Sons |
| Total Pages | : 448 |
| Release | : 2020-07-08 |
| Genre | : Computers |
| ISBN | : 1119711576 |
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.
