Recommender Systems

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

User Centric Media

Author: Petros Daras
Publisher: Springer Science & Business Media
ISBN: 3642126294
Format: PDF, Kindle
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This book constitutes the thoroughly refereed post-conference proceedings of the First International Conference, UCMedia 2009, which was held on 9-11 December 2009 at Hotel Novotel Venezia Mestre Castellana in Venice, Italy. The conference`s focus was on forms and production, delivery, access, discovery and consumption of user centric media. After a thorough review process of the papers received, 23 were accepted from open call for the main conference and 20 papers for the workshops.

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.

Destination Recommendation Systems

Author: Daniel R. Fesenmaier
Publisher: CABI
ISBN: 1845931092
Format: PDF, Kindle
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An emerging area of study within technology and tourism focuses on the development of technologies which enable Internet users to quickly and effectively find relevant information about selected topics including travel destination, transportation, etc. This area of tourism research and development is generally referred to as destination marketing systems (DMSs) and brings together both applied and academic interests ranging from marketing and management to psychology, mathematics and computer sciences. This book provides a comprehensive synthesis of the current status of research, representing the contributions of some of the leading researchers in destination marketing systems.

Recommender Systems

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

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.

Predicting movie ratings and recommender systems

Author: Arkadiusz Paterek
Publisher: Arkadiusz Paterek
ISBN:
Format: PDF
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A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.

Web Page Recommendation Models

Author: Şule Gündüz-Ögüdücü
Publisher: Morgan & Claypool Publishers
ISBN: 1608452476
Format: PDF, Docs
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This monograph gives an overview of the research in the area of discovering and modeling the users' interest in order to recommend related Web pages. The Web page recommender systems studied in this monograph are categorized according to the data mining algorithms they use for recommendation. One of the application areas of data mining: the World Wide Web (WWW) serves as a huge, widely distributed, global information service center for every kind of information (e.g., news, advertisements, consumer information, financial management, education, government, e-commerce, and health services). The amount of information on the Web is also growing rapidly, along with the number of Web sites and Web pages per Web site. This growth makes it more difficult to find relevant and useful information to be used as a guide for Web users to discover useful knowledge that supports decision-making. Therefore, the ability to predict the needs of a Web user as (s)he visits Web sites has gained importance.

Recommender Systems

Author: Gérald Kembellec
Publisher: John Wiley & Sons
ISBN: 1119054230
Format: PDF, Mobi
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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.