Statistical Methods for Recommender Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Methods for Recommender Systems write by Deepak K. Agarwal. This book was released on 2016-02-24. Statistical Methods for Recommender Systems available in PDF, EPUB and Kindle. 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.
Review and Implementation of Common Statistical Methods for Recommender Systems
Review and Implementation of Common Statistical Methods for Recommender Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Review and Implementation of Common Statistical Methods for Recommender Systems write by Candace Jennifer McKeag. This book was released on 2021. Review and Implementation of Common Statistical Methods for Recommender Systems available in PDF, EPUB and Kindle. As a result of today's massive information overload, the exploration and development of recommender systems is burgeoning. This paper consists of a comprehensive literature review in which the current knowledge surrounding statistical methods for recommender systems is outlined and evaluated. For each method, the theoretical premise and application-related aspects such as optimal use cases and common research problems are described. To round out the literature review, an implementation of several collaborative filtering techniques is conducted in order to apply the discussed theory and identify some advantages and disadvantages of the methods.
Recommender Systems
Recommender Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Recommender Systems write by Charu C. Aggarwal. This book was released on 2016-03-28. Recommender Systems available in PDF, EPUB and Kindle. 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 Handbook
Recommender Systems Handbook - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Recommender Systems Handbook write by Francesco Ricci. This book was released on 2015-11-17. Recommender Systems Handbook available in PDF, EPUB and Kindle. 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 for Information Providers
Recommender Systems for Information Providers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Recommender Systems for Information Providers write by Andreas W. Neumann. This book was released on 2009-03-03. Recommender Systems for Information Providers available in PDF, EPUB and Kindle. Information providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications. Specific solutions for areas with strong privacy concerns, scalability issues for large collections of products, as well as algorithms to lessen the cold-start problem for a faster return on investment of recommender projects are addressed. This book describes all steps it takes to design, implement, and successfully operate a recommender system for a specific information platform.
