Collaborative Filtering Recommender Systems
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Collaborative Filtering Recommender Systems
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Author : Michael D. Ekstrand
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
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.
The Adaptive Web
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Author : Peter Brusilovski
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-04-24
The Adaptive Web written by Peter Brusilovski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-24 with Computers categories.
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.
Study On Bipartite Network In Collaborative Filtering Recommender System
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Author : Luqi Yao
language : en
Publisher:
Release Date : 2015
Study On Bipartite Network In Collaborative Filtering Recommender System written by Luqi Yao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Recommender system is increasingly popular in recent years, scientists came up with plenty of recommendation algorithms and never stop trying to make the recommendation more accurate. Although the relationship within recommender systems could usually be represented as a network which is few people tried to build recommender systems based on the properties of the network. This might be a potential area that is underdeveloped. In this thesis, the projection of a weighted bipartite graph used in the collaborative filtering recommender system will be presented. Based on previous work in the related field, the similarity function of general collaborative filtering is redefined using the resource allocation process on the weighted bipartite graph. The process of resource allocation is implemented by a two-step random walk which calculates the recommendation power between each user. The recommendation power within the set of users is used to generate the specific similarity function for collaborative filtering. The proposed new method is used in the MoiveLen dataset which is a benchmark dataset and its performance is compared with four widely used methods: collaborative filtering using Pearson correlation, collaborative filtering using cosine similarity, user-mean, and item-mean by comparing the value of MAE and RMSE. However, the results is not as good as expected. The performance of projected bipartite method is not outstanding. It needs to be improved in the future.
Recommender Systems Handbook
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Author : Francesco Ricci
language : en
Publisher: Springer
Release Date : 2015-11-17
Recommender Systems Handbook written by Francesco Ricci and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-17 with Computers categories.
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
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Author : P. Pavan Kumar
language : en
Publisher: CRC Press
Release Date : 2021-06-01
Recommender Systems written by P. Pavan Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-01 with Computers categories.
Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.
Enhancing The Diversity Of Collaborative Filtering Recommender Systems
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Author : Mi Zhang
language : en
Publisher:
Release Date : 2010
Enhancing The Diversity Of Collaborative Filtering Recommender Systems written by Mi Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Personalization Techniques And Recommender Systems
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Author : Gulden Uchyigit
language : en
Publisher: World Scientific
Release Date : 2008
Personalization Techniques And Recommender Systems written by Gulden Uchyigit and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Science categories.
The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed.The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems.This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.
Recommender Systems
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Author : Gérald Kembellec
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-15
Recommender Systems written by Gérald Kembellec and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-15 with Computers categories.
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.
Collaborative Recommendations Algorithms Practical Challenges And Applications
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Author : Shlomo Berkovsky
language : en
Publisher: World Scientific
Release Date : 2018-11-30
Collaborative Recommendations Algorithms Practical Challenges And Applications written by Shlomo Berkovsky and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.
Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.
Group Recommender Systems
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Author : Alexander Felfernig
language : en
Publisher: Springer
Release Date : 2018-03-07
Group Recommender Systems written by Alexander Felfernig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-07 with Technology & Engineering categories.
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.